{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example notebook for the %%stata cell magic by the IPyStata package. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:**   Ties de Kok <t.c.j.dekok@tilburguniversity.edu>  \n",
    "**Twitter:** @TiesdeKok <https://twitter.com/TiesdeKok/>  \n",
    "**Homepage:**    https://github.com/TiesdeKok/ipystata  \n",
    "**PyPi:** https://pypi.python.org/pypi/ipystata  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note: this example notebook uses the Windows only `Stata Automation` mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Github for an example notebook that uses the `Stata Batch Mode` (supported for Windows, Mac OS X, and Linux)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.CodeCell.config_defaults.highlight_modes['magic_stata'] = {'reg':[/^%%stata/]};"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipystata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: You can ignore the `Javascript error adding output!` warning if it pops up with the newest version of Jupyter Notebook *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure ipystata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that you have registered your Stata instance. (See GitHub for instructions). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check whether IPyStata is working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hello, I am printed by Stata.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "\n",
    "display \"Hello, I am printed by Stata.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some examples based on the Stata 13 manual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset \"auto.dta\" in Stata return it back to Python as a Pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code cell below runs the Stata command **`sysuse auto.dta`** to load the dataset and returns it back to Python via the **`-o car_df`** argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1978 Automobile Data)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -o car_df\n",
    "sysuse auto.dta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`car_df`** is a regular Pandas dataframe on which Python / Pandas actions can be performed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>price</th>\n",
       "      <th>mpg</th>\n",
       "      <th>rep78</th>\n",
       "      <th>headroom</th>\n",
       "      <th>trunk</th>\n",
       "      <th>weight</th>\n",
       "      <th>length</th>\n",
       "      <th>turn</th>\n",
       "      <th>displacement</th>\n",
       "      <th>gear_ratio</th>\n",
       "      <th>foreign</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMC Concord</td>\n",
       "      <td>4099</td>\n",
       "      <td>22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2930</td>\n",
       "      <td>186</td>\n",
       "      <td>40</td>\n",
       "      <td>121</td>\n",
       "      <td>3.58</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Pacer</td>\n",
       "      <td>4749</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3350</td>\n",
       "      <td>173</td>\n",
       "      <td>40</td>\n",
       "      <td>258</td>\n",
       "      <td>2.53</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMC Spirit</td>\n",
       "      <td>3799</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2640</td>\n",
       "      <td>168</td>\n",
       "      <td>35</td>\n",
       "      <td>121</td>\n",
       "      <td>3.08</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Buick Century</td>\n",
       "      <td>4816</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>16</td>\n",
       "      <td>3250</td>\n",
       "      <td>196</td>\n",
       "      <td>40</td>\n",
       "      <td>196</td>\n",
       "      <td>2.93</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Buick Electra</td>\n",
       "      <td>7827</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4080</td>\n",
       "      <td>222</td>\n",
       "      <td>43</td>\n",
       "      <td>350</td>\n",
       "      <td>2.41</td>\n",
       "      <td>Domestic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            make  price  mpg  rep78  headroom  trunk  weight  length  turn  \\\n",
       "0    AMC Concord   4099   22    3.0       2.5     11    2930     186    40   \n",
       "1      AMC Pacer   4749   17    3.0       3.0     11    3350     173    40   \n",
       "2     AMC Spirit   3799   22    NaN       3.0     12    2640     168    35   \n",
       "3  Buick Century   4816   20    3.0       4.5     16    3250     196    40   \n",
       "4  Buick Electra   7827   15    4.0       4.0     20    4080     222    43   \n",
       "\n",
       "   displacement  gear_ratio   foreign  \n",
       "0           121        3.58  Domestic  \n",
       "1           258        2.53  Domestic  \n",
       "2           121        3.08  Domestic  \n",
       "3           196        2.93  Domestic  \n",
       "4           350        2.41  Domestic  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic descriptive statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument **`-d or --data`** is used to define which dataframe should be set as dataset in Stata.  \n",
    "In the example below the Stata function **`tabulate`** is used to generate some descriptive statistics for the dataframe **`car_df`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           |                        headroom\n",
      "   foreign |       1.5          2        2.5          3        3.5 |     Total\n",
      "-----------+-------------------------------------------------------+----------\n",
      "  Domestic |         3         10          4          7         13 |        52 \n",
      "   Foreign |         1          3         10          6          2 |        22 \n",
      "-----------+-------------------------------------------------------+----------\n",
      "     Total |         4         13         14         13         15 |        74 \n",
      "\n",
      "           |             headroom\n",
      "   foreign |         4        4.5          5 |     Total\n",
      "-----------+---------------------------------+----------\n",
      "  Domestic |        10          4          1 |        52 \n",
      "   Foreign |         0          0          0 |        22 \n",
      "-----------+---------------------------------+----------\n",
      "     Total |        10          4          1 |        74 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -d car_df\n",
    "tabulate foreign headroom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These descriptive statistics can be replicated in Pandas using the **`crosstab`** fuction, see the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>headroom</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.0</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.0</th>\n",
       "      <th>3.5</th>\n",
       "      <th>4.0</th>\n",
       "      <th>4.5</th>\n",
       "      <th>5.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foreign</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Domestic</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foreign</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "headroom  1.5  2.0  2.5  3.0  3.5  4.0  4.5  5.0  All\n",
       "foreign                                              \n",
       "Domestic    3   10    4    7   13   10    4    1   52\n",
       "Foreign     1    3   10    6    2    0    0    0   22\n",
       "All         4   13   14   13   15   10    4    1   74"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(car_df['foreign'], car_df['headroom'], margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stata graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPyStata will automatically check whether there are any new graph generated.  \n",
    "If you want to show multiple graphs, you have to make sure to use the `, name(.., replace)`argument in your Stata code.  \n",
    "**Note: the order is not guaranteed to be the same as the generation order. Recommended to use the `title()` argument when showing multiple graphs.**\n",
    "\n",
    "It is possible to prevent graphs from showing using the `-nogr` or `--nograph` arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(highschool and beyond (200 cases))\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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AnkDKPR8P+j+sgZR7Ph70fxQMESDlno8H/acSkHLPx4P+j4KBBq8+faYiGm24\nE4tGG+6jYBSMglFATyDlno8H/R/WQMo9Hw/6PwqGCJByz8eD/lMJSLnn40H/R8FAg1efPlMRjTbc\niUWjDfdRMApGwSigJ5Byz8eD/g9rIOWejwf9HwVDBEi55+NB/6kEpNzz8aD/o2CgwatPn6mIRhvu\nxKLRhvsoGAWjYBTQH0i556Oh/yMGSLnno6H/o2AIAin3fDT0nwZAyj0fDf0fBYMDvPr0mYpotOFO\nLBptuI+CUTAKRsEoGAWjYBSMApLAq0+fqYhGG+7EotGG+ygYBaNgFIyCUTAKRsEoIAm8+vSZimi0\n4U4sGm24j4JRMApGwSgYBaNgFIwCksCrT5+piEYb7sSi0Yb7KBgFo2AUjIJRMApGwSggCbz69JmK\naLThTiwabbiPglEwCkbBKBgFo2AUjAKSwKtPn6mIRhvuxKLRhvsoGAWjYBSMglEwCkbBKCAJvPr0\nmYpotOFOLBptuI+CUTAKRsEoGAWjYBSMApLAq0+fqYhGG+7EotGG+ygYBaNgFIyCUTAKRsEoIAm8\n+vSZimi04U4sGm24j4JRMApGwSgYBaNgFIwCksCrT5+piEYb7sSi0Yb7KBgFo2AUjIJRMPzAoL1k\ndNA6bBSQBF59+kxFNNpwJxaNNtxHwSgYBaNgFIyC4QSk3PPR0P/BAaTc89HQ/1EwZMGrT5+piEYb\n7sSi0Yb7KBgFo2AUjIJRMDyAlHs+HvR/4ICUez4e9H8UDEHw6tNnKqLRhjuxaLThPgpGwSgYBaNg\nFAwPIOWejwf9Hzgg5Z6PB/0fBUMQvPr0mYpoiDbcLzabMeAGics/fX5FbcQw2nAfBaNgFIyCUTAK\nhgWQcs/Hg/4PHJByz8eD/o+CIQheffpMRTTEGu7LExlIAInrXn36TC3EMNpwHwWjYBSMglEwCoY+\nkHLPJ4j+DwSQcs8niP6PgqEGXn36TEU0hBruyKPsZs3nP7/6hAuti2OAAbPOk58+v6IGYhhtuI+C\nUTAKRsEoGAVDH0i55xNE/wcCSLnnE0T/R8FQA68+faYiGjoN9zXQwfa4NZ9ffSICne80YWBgYGAw\n6bj46tNnyhHDaMN9FIyCUTAKRsEoGPpAyj2fIPo/EEDKPZ8g+j8Khhp49ekzFdGQabhDFsmQ1Ao/\n2WHGwEC1QXeG0Yb7KBgFo2AUjIJRMPSBlHs+QfR/IICUez5B9H8UDDXw6tNnKqKh0nCHrH7Bv0IG\nA0EH3amzV5VhtOE+CkbBKBgFo2AUDAsg5Z6PB/0fOCDlno8H/R8FQxC8+vSZimhoNdxJbYKTpws7\nYhhtuI+CUTAKRsEoGAXDAki55+NB/wcOSLnn40H/R8EQBK8+faYiGloN99ER91EwCkbBKBgFo2AU\nUAqk3PPxoP8DB6Tc8/Gg/6NgCIJXnz5TEQ2Vhjv0SJnRNe6jYBSMglEwCkbBKKAKkHLPR0P/BweQ\ncs9HQ/9HwZAFrz59piIaKg13WCuc+EF36HA7aW19PIhhtOE+CkbBKBgFo2AUjIJRMApIAa8+faYi\nGjINd/igOwMDA6HVL5B1NQwMDAzUGm5/9Wm04T4KRsEoGAWjYBSMglEwCkgDrz59piIaQg33z69g\nh0ISC0ZvTh0Fo2AUjIJRMApGwSgYBQMHXn36TEU0xBruMIQ0po4F4B+PJxMxjDbcR8EoGAWjYBSM\nglEwCkYBKeDVp89UREO04T4AiIFhNKxG0SgaRaNoFI2iUTSKRtGAodHGKAIxEAKvPn0eRaNoFI2i\nUTSKRtEoGkWjaEDQaGOUWMQwulRmFIyCUTAKRsEoGAWjYBSQAl59+kxFNFQa7tBF7dQ625EMxDDa\ncB8Fo2AUjIJRMApGwSgYBaSAV58+UxENsYY7AwM1T3gkCTGMNtxHwSgYBaNgFIyCUTAKRgEp4NWn\nz1REQ6zhbtLRGcfAwED8NUzUQwyjDfdRMApGwSgYBaNgFIyCUUAKePXpMxXRkGu4X0TcxETVY9oJ\nIobRhvsoGAWjYBSMglEwCkbBKCAFvPr0mYpoKDbcP7/69PlkB6TxjhChNWIYbbiPglEwCkbBKBgF\no2AUjAJSwKtPn6mIhmrD/dWnz6/Od5owQAGKOG0Qw2jDfRSMglEwCkbBKBgFo2AUkAJeffpMRTSU\nG+6fPr9CGnpnYGCg6b5VhtGG+ygYBaNgFIyCUTAKRsEoIAW8+vSZimjIN9whCKX5zsDAwMAQt+bz\nq0/URAyjDfdRMApGwSgYBaNgFIyCUUAKePXpMxXRMGm4wxBUGSpIXP7p8yuKEcNow30UjIJRMApG\nwSgYBaNgFJACXn36TEU0zBruCLQ8kQEGRhvuo2AUjIJRMApGwSgYBaNgAMCrT5+piIZtw53qiGG0\n4T4KRsEoGAWjYBSMglEwCkgBrz59piIabbgTixhGG+6jYBSMglEwCkbBKBgFo4AU8OrTZyqi0YY7\nsYhhtOE+CkbBKBgFo2AUjIJRMApIAa8+faYiGioN94FHow33UTAKRsEoGAWjYBSMglFAEnj16TMV\n0WjDnVg02nAfBaNgFAwtIOWe/39QAvo4jD62kArIcBUZWsgAZNhChpaRDIZNcA0bj9ANvPr0mYpo\ntOFOLBptuI+CUTAKhgqQcs9HQ/8HB5Byz0dD/2kApNzz0dD/QQCk3PPR0H9CQMo9Hw39pwGQcs9H\nQ/8JASn3fDT0fxTgBlLu+Wjo/9AEUu75aOj/KCACvPr0mYpotOFOLBptuI+CUTAKBj+Qcs/Hg/4P\nHJByz8eD/lMJSLnn40H/BwhIuefjQf+xASn3fDzoP5WAlHs+HvQfG5Byz8eD/o8CVCDlno8H/R86\nQMo9Hw/6PwrwglefPlMRjTbciUWjDfdRMApGweAHUu75eND/gQNS7vl40H8qASn3fDzo/wABKfd8\nPOg/NiDlno8H/acSkHLPx4P+YwNS7vl40P9RgAqk3PPxoP9DB0i55+NB/0cBXvDq02cqotGGO7Fo\ntOE+CkbBKBj8QMo9Hw/6P3BAyj0fD/pPJSDlno8H/R8gIOWejwf9xwak3PPxoP9UAlLu+XjQf2xA\nyj0fD/o/ClCBlHs+HvR/6AAp93w86P8owAteffpMRTTacCcWjTbcR8EoGAWDHEi55xNE/wcCSLnn\nE0T/KQZS7vkE0X+6Ayn3fILoPyqQcs8niP5TDKTc8wmi/6hAyj2fIPo/CmBAyj2fIPo/FICUez5B\n9H8U4AavPn2mIhptuBOLRhvuo2AUjIJBDqTc8wmi/wMBpNzzCaL/FAMp93yC6D/dgZR7PkH0HxVI\nuecTRP8pBlLu+QTRf1Qg5Z5PEP0fBTAg5Z5PEP0fCkDKPZ8g+j8KcINXnz5TEY023IlFow33UTAK\nRsEgB1Lu+QTR/4EAUu75BNF/ioGUez5B9J/uQMo9nyD6jwqk3PMJov8UAyn3fILoPyqQcs8niP6P\nAhiQcs8niP4PBSDlnk8Q/R8FuMGrT5+piEYb7sSi0Yb7KBgFo2CQAyn3fILo/0AAKfd8gug/xUDK\nPZ8g+k93IOWeTxD9RwVS7vkE0X+KgZR7PkH0HxVIuecTRP9HAQxIuecTRP+HApByzyeI/o8C3ODV\np89URKMNd2LRaMN9FIyCUTD4gZR7Ph70f+CAlHs+HvSfSkDKPR8P+j9AQMo9Hw/6jw1IuefjQf+p\nBKTc8/Gg/9iAlHs+HvR/FKACKfd8POj/0AFS7vl40P9RgBe8+vSZimi04U4sGm24j4JRMAoGP5By\nz8eD/g8ckHLPx4P+UwlIuefjQf8HCEi55+NB/7EBKfd8POg/lYCUez4e9B8bkHLPx4P+jwJUIOWe\njwf9HzpAyj0fD/o/CvCCV58+UxGNNtyJRaMN91EwCkbB4AdS7vl40P+BA1Lu+XjQfyoBKfd8POj/\nAAEp93w86D82IOWejwf9pxKQcs/Hg/5jA1Lu+XjQ/1GACqTc8/Gg/0MHSLnn40H/RwFe8OrTZyqi\n0YY7sWi04T4KRsEoGCpAyj0fDf0fHEDKPR8N/acBkHLPR0P/BwGQcs9HQ/8JASn3fDT0nwZAyj0f\nDf0nBKTc89HQ/1GAG0i556Oh/0MTSLnno6H/o4AI8OrTZyqi0YY7sWi04T4KRsEoGAWjYBSMglEw\nCkgCrz59piIabbgTi0Yb7qNgFIyCUTAKRsEoGAWjgCTw6tNnKqLRhjuxaLThPgpGwSgYBaNgFIyC\nUTAKSAKvPn2mIhptuBOLRhvuo2AUjIJRMApGwSgYBaOAJPDq02cqotGGO7FotOE+CkbBKBgFo2AU\njIJRMApIAq8+faYiGm24E4tGG+6jYBSMglEwCkbBKBgFo4Ak8OrTZyqi0YY7sWi04T4KRsEoGAWj\nYBSMglEwCkgCrz59piIabbgTi0Yb7qNgFIyCUTAKRsEoGAWjgCTw6tNnKqLRhjuxaLThPgpGwSgY\nBaNgFIyCUTAKSAKvPn2mIhptuBOLRhvuo2AUjIJRMApGAT3B6N2co2AYgFefPlMRjTbciUWjDfdR\nMApGwSgYBaOAPkDKPR8N/R8Fo2BoglefPlMRjTbciUWjDfdRMApGwSgYBaOA1kDKPR8P+j8KRsFQ\nA68+faYiGm24E4tGG+6jYBSMglEwCkYBrYGUez4e9H8UjIKhBl59+kxFNNpwJxaNNtxHwSgYBaNg\nFIwCWgMp93w86P8oGAVDDbz69JmKaLThTiwabbiPglEwCkbBKBgFNAVS7vkE0f9RMAqGFHj16TMV\n0WjDnVg02nAfBaNgFIyCUTAKaAqk3PMJov+jYBQMKfDq02cqotGGO7FotOE+CkbBKBgFo2AU0BRI\nuecTRP9HwSgYUuDVp89URKMNd2LRaMN9FIyCUTAKRsEooCmQcs8niP6PglEwpMCrT5+piEYb7sSi\n0Yb7KBgFo2AUjIJRQGsg5Z6PB/0fBaNgqIFXnz5TEY023IlFow33UTAKRsEoGAWjgNZAyj0fD/o/\nCkbBUAOvPn2mIhptuBOLRhvuo2AUDEUwWtMPDzBs4pE+HiHDFjK0kAqItELKPR8P+k8IEKOGckAf\nW0bB8ACvPn2mIhptuBOLRhvuo2AUDC0g5Z6Phv6PgiEIpNzz0dD/oQmk3PPR0H8aACn3fDT0nxCQ\ncs9HQ/+pDaTc89HQf0JAyj0fDf0nBKTc89HQfxoAKfd8NPR/FIwCvODVp89URKMNd2LRaMN9FIyC\noQKk3PPxoP+jYIgAKfd8POj/0AFS7vl40H8qASn3fDzoPzYg5Z6PB/2nBpByz8eD/lMJSLnn40H/\nqQSk3PPxoP+jYBTgAK8+faYiGm24E4tGG+6jYBQMFSDlno8H/R8FQwRIuefjQf+HDpByz8eD/lMJ\nSLnn40H/sQEp93w86D81gJR7Ph70n0pAyj0fD/pPJSDlno8H/R8FowAHePXpMxXRaMOdWDTacB8F\no2CoACn3fDzo/ygYIkDKPR8P+j90gJR7Ph70n0pAyj0fD/qPDUi55+NB/6kBpNzz8aD/VAJS7vl4\n0H8qASn3fDzo/ygYBTjAq0+fqYhGG+7EotGG+ygYBUMCSLnnE0T/R8GgB1Lu+QTR/6EApNzzCaL/\nFAMp93yC6D8qkHLPJ4j+Uwak3PMJov8UAyn3fILoP8VAyj2fIPo/CkYBNvDq02cqotGGO7FotOE+\nCkbBkABS7vkE0f9RMOiBlHs+QfR/KAAp93yC6D/FQMo9nyD6jwqk3PMJov+UASn3fILoP8VAyj2f\nIPpPMZByzyeI/o+CUYANvPr0mYpotOFOLBptuI+CUTAkgJR7PkH0fxQMeiDlnk8Q/R8KQMo9nyD6\nTzGQcs8niP6jAin3fILoP2VAyj2fIPpPMZByzyeI/lMMpNzzCaL/o2AUYAOvPn2mIhptuBOLRhvu\no2AUDAkg5Z5PEP0fBYMeSLnnE0T/hwKQcs8niP5TDKTc8wmi/6hAyj2fIPpPGZByzyeI/lMMpNzz\nCaL/FAMp93yC6P8oGAXYwKtPn6mIRhvuxKLRhvsoGAVDBUi55+NB/0fBEAFS7vl40P+hA6Tc8/Gg\n/1QCUu75eNB/bEDKPR8P+k8NIOWejwf9pxKQcs/Hg/5TCUi55+NB/0fBKMABXn36TEU02nAnFo02\n3EfBKBgqQMo9Hw/6PwqGCJByz8eD/g8dIOWejwf9pxKQcs/Hg/5jA1Lu+XjQf2oAKfd8POg/lYCU\nez4e9J9KQMo9Hw/6PwpGAQ7w6tNnKqLRhjuxaLThPgpGwVABUu75eND/UTBEgJR7Ph70f+gAKfd8\nPOg/lYCUez4e9B8bkHLPx4P+UwNIuefjQf+pBKTc8/Gg/1QCUu75eND/UTAKcIBXnz5TEY023IlF\now33UTAKhhaQcs9HQ/9HwRAEUu75aOj/0ARS7vlo6D8NgJR7Phr6TwhIueejof/UBlLu+WjoPw2A\nlHs+GvpPAyDlno+G/o+CUYAXvPr0mYpotOFOLBptuI+CUTAKRsEoGAWjYBSMApLAq0+fqYhGG+7E\notGG+ygYBaNgFIyCUTAKRsEoIAm8+vSZimi04U4sGm24j4JRMApGwSgYBaNgFIwCksCrT5+piEYb\n7sSi0Yb7KBgFo2AUjIJRMApGwSggCbz69JmKaLThTiwabbiPglEwCkbBKBgFo2AUjAKSwKtPn6mI\nRhvuxKLRhvsoGAWjYBSMglEwCkbBKCAJvPr0mYpotOFOLBptuI+CUTAKRsEoGAWjYBSMApLAq0+f\nqYhGG+7EotGG+ygYBaNgFIyCUTAKRsEoIAm8+vSZimi04U4sGm24j4JRMApGwSgYBaNgFIwCksCr\nT5+piEYb7sSi0YY7kWD0GrlRQDtARuqij5ZhA8jw+6DVQiqggxXDDAybEKOPR+hjCx3AsPEI3cCr\nT5+piEYb7sSi0YY7QSDlno+G/o+CUUAlIOWej4b+EwJS7vlo6D8hIOWej4b+jxgg5Z6Phv4TAlLu\n+WjoPyEg5Z6Phv4TAlLu+WjoP7WBlHs+Gvo/CvACKfd8NPR/aAIp93w09J8GQMo9Hw39H5pAyj0f\nDf0fBUSAV58+UxGNNtyJRaMNdzxAyj0fD/o/CkYBBUDKPR8P+o8NSLnn40H/sQEp93w86P+wBlLu\n+XjQf2xAyj0fD/qPDUi55+NB/7EBKfd8POg/NYCUez4e9H8UYAAp93w86P/QAVLu+XjQfyoBKfd8\nPOj/0AFS7vl40P9RgBe8+vSZimi04U4sGm244wFS7vl40P9RMAooAFLu+XjQf2xAyj0fD/qPDUi5\n5+NB/4c1kHLPx4P+YwNS7vl40H9sQMo9Hw/6jw1IuefjQf+pAaTc8/Gg/6MAA0i55+NB/4cOkHLP\nx4P+UwlIuefjQf+HDpByz8eD/o8CvODVp89URKMNd2LRaMMdD5Byz8eD/o+CUUABkHLPx4P+YwNS\n7vl40H9sQMo9Hw/6P6yBlHs+HvQfG5Byz8eD/mMDUu75eNB/bEDKPR8P+k8NIOWejwf9HwUYQMo9\nHw/6P3SAlHs+HvSfSkDKPR8P+j90gJR7Ph70fxTgBa8+faYiGm24E4tGG+64gJR7PkH0fxSMArKA\nlHs+QfQfFUi55xNE/1GBlHs+QfR/mAIp93yC6D8qkHLPJ4j+owIp93yC6D8qkHLPJ4j+Uwak3PMJ\nov+jAAlIuecTRP+HApByzyeI/lMMpNzzCaL/QwFIuecTRP9HAW7w6tNnKqLRhjuxaLThjgtIuecT\nRP9HwSggC0i55xNE/1GBlHs+QfQfFUi55xNE/4cpkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC\n6D9lQMo9nyD6PwqQgJR7PkH0fygAKfd8gug/xUDKPZ8g+j8UgJR7PkH0fxTgBq8+faYiGm24E4tG\nG+64gJR7PkH0fxSMArKAlHs+QfQfFUi55xNE/1GBlHs+QfR/mAIp93yC6D8qkHLPJ4j+owIp93yC\n6D8qkHLPJ4j+Uwak3PMJov+jAAlIuecTRP+HApByzyeI/lMMpNzzCaL/QwFIuecTRP9HAW7w6tNn\nKqLRhjuxaLThjgtIuecTRP9HwSggC0i55xNE/1GBlHs+QfQfFUi55xNE/4cpkHLPJ4j+owIp93yC\n6D8qkHLPJ4j+owIp93yC6D9lQMo9nyD6PwqQgJR7PkH0fygAKfd8gug/xUDKPZ8g+j8UgJR7PkH0\nfxTgBq8+faYiGm24E4tGG+54gJR7Ph70fxSMAgqAlHs+HvQfG5Byz8eD/mMDUu75eND/YQ2k3PPx\noP/YgJR7Ph70HxuQcs/Hg/5jA1Lu+XjQf2oAKfd8POj/KMAAUu75eND/oQOk3PPxoP9UAlLu+XjQ\n/6EDpNzz8aD/owAvePXpMxXRaMOdWDTacMcDpNzz8aD/o2AUUACk3PPxoP/YgJR7Ph70HxuQcs/H\ng/4PayDlno8H/ccGpNzz8aD/2ICUez4e9B8bkHLPx4P+UwNIuefjQf9HAQaQcs/Hg/4PHSDlno8H\n/acSkHLPx4P+Dx0g5Z6PB/0fBXjBq0+fqYhGG+7EotGGOx4g5Z6PB/0fBaOAAiDlno8H/ccGpNzz\n8aD/2ICUez4e9H9YAyn3fDzoPzYg5Z6PB/3HBqTc8/Gg/9iAlHs+HvSfGkDKPR8P+j8KMICUez4e\n9H/oACn3fDzoP5WAlHs+HvR/6AAp93w86P8owAteffpMRTTacCcWjTbcCQIp93w09H8UjAIqASn3\nfDT0nxCQcs9HQ/8JASn3fDT0f8QAKfd8NPSfEJByz0dD/wkBKfd8NPSfEJByz0dD/6kNpNzz0dD/\nUYAXSLnno6H/QxNIueejof80AFLu+Wjo/9AEUu75aOj/KCACvPr0mYpotOFOLBptuI+CUTAKRsEo\nGAWjYBSMApLAq0+fqYhGG+7EotGG+ygYBaNgFIyCUTAKRsEoIAm8+vSZimi04U4sGm24j4JRMApG\nwSgYBaNgFIwCksCrT5+piEYb7sSi0Yb7KBgFo2AUjIJRMApGwSggCbz69JmKaLThTiwabbiPglEw\nCkbBKBgFo2AUjAKSwKtPn6mIRhvuxKLRhvsoGAWjYBSMglEwCkbBKCAJvPr0mYpotOFOLBptuI+C\nUTAKRsEoGAWjYBSMApLAq0+fqYhGG+7EotGG+ygYBaNgFIyCUTAKRsEoIAm8+vSZimi04U4sGm24\nj4JRMApGwSgYBaNgFIwCksCrT5+piEYb7sSi0Yb7KBgFo2AUjIJRMApGwSggCbz69JmKaAAa7ssT\nGWDArPn851efPp/sMGNIXPfq0+fBjBhGG+6jYBSMDDB6j/coGAWjYBSMAmqBV58+UxHRteF+ssOM\nAQVAG+6wpnzi8k+fXw1WxDDacB8Fo2C4Ayn3fDT0fxSMglEwCkbBKKAAvPr0mYqIfg13eKs9bs3n\nV5/WxTEwwBvuMC4Dg1nnyU+fXw1KxDDacB8Fo2D4Ain3fDzo/ygYBaNgFIyCUUAWePXpMxURvRru\n5ztNGBiQxtQhLXV4w/0zTITBpOPiq0+fByFiGG24j4JRMHyBlHs+HvR/FIyCUTAKRsEoIAu8+vSZ\niohODXfIYhikRjmkmY7ccIcNyRO12P1isxkDbgDvHlATMYw23EfBKBi+QMo9Hw/6PwpGwSgYBaNg\nFJAFXn36TEVEn4Y7pJ2N3EzH0nDHGJXHgiAdAGIBUX0AYhHDaMN9FIyCYQqk3PMJov+jYBSMglEw\nCkYB6eDVp89URPRpuEOa6cgtcogIasMdKoisDBlBWv8QgKYRDUHMYWBgYKDionmG0Yb7KBgFwxRI\nuecTRP9HwSgYBaNgFIwC0sGrT5+piAZTwx3/iPsa6GB73JrPrz4RgaCmIa/PoQgxjDbcR8EoGKZA\nyj2fIPo/CkbBKBgFo2AUkA5effpMRUSfhjtksBy5mY6t4Q5pmuMYI4cskiGpFQ5dNI/DQFIRw2jD\nfRSMgmEKpNzzCaL/o2AUjIJRMApGAeng1afPVET0abhjNruxNNwhanAsTMeinjCCDrrjGMInETGM\nNtxHwSgYvkDKPR8P+j8KRsEoGAWjYBSQBV59+kxFRKeGO6wNDW95ozfEoaPjSCKoCKKe1CY4ebqw\nI4bRhvsoGAXDF0i55+NB/0fBKBgFo2AUjAKywKtPn6mI6NVwRzTNIYvUIU1qSDMdspCGgQFlSB4N\nIasnGkF7C6MN91EwCkYBASDlno8H/ScEiFGDBsjQMpIBGcFFhhZSAR2s+E8vWwYnGMl+HwXDBrz6\n9JmKiH4N91dIbXesAHer/TO8cY9XDTqCWje6xn0UjIJRQByQcs9HQ/8JASn3fDT0nxCQcs9HQ/9H\nAW4g5Z6Phv4TAlLu+WjoP7WBlHs+GvpPAyDlno+G/o8YIOWej4b+j4JRMDTBq0+fqYjo2nCHIOha\ndmRARNsa2gonftAdOtxOWlsfD2IYbbiPglEwfIGUez4e9B8bkHLPx4P+YwNS7vl40P9RgAqk3PPx\noP/YgJR7Ph70nxpAyj0fD/pPJSDlno8H/R/WQMo9Hw/6PwpGwVADrz59piIagIY7uQg66M7AwEBo\n9QtkXQ0DAwMDMV0CIhHDaMN9FIyC4Quk3PPxoP/YgJR7Ph70HxuQcs/Hg/6PAlQg5Z6PB/3HBqTc\n8/Gg/9QAUu75eNB/KgEp93w86P+wBlLu+XjQ/1EwCoYaePXpMxURnRruyxMZCLW2iULLExlIANgP\nqCETMYw23EfBKBi+QMo9Hw/6jw1IuefjQf+xASn3fDzo/yhABVLu+XjQf2xAyj0fD/pPDSDlno8H\n/acSkHLPx4P+D2sg5Z6PB/0fBaNgqIFXnz5TEdGn4Q4dAqfWqhW4gTgAFXoImIhhtOE+CkbBMAVS\n7vkE0X9UIOWeTxD9RwVS7vkE0f9RAANS7vkE0X9UIOWeTxD9pwxIuecTRP8pBlLu+QTR/2EKpNzz\nCaL/o2AUDCnw6tNnKiK6Ntzj1nx+9WmoIgYG+oTVKBpFo4jeSMo9nyB69ekzMpJyzyeIXn36jIyk\n3PMJolefPo8iCJJyzyeIXn36jIyk3PMJolefPlOCpNzzCaJXnz5TiKTc8wmiV58+D0sk5Z5PEL36\n9HkUjaIRi+jUGIUucaHq2hWqIwZC4NWnz6NoFI2i4Yek3PMJolefPiMjKfd8gujVp8/ISMo9nyB6\n9enzKIIgKfd8gujVp8/ISMo9nyB69ekzJUjKPZ8gevXpM4VIyj2fIHr16fOwRFLu+QTRq0+fR9Eo\nGrGIfo1R2LEwNFnHQgfEMLpUZhSMgmEKpNzzCaL/qEDKPZ8g+o8KpNzzCaL/owAGpNzzCaL/qEDK\nPZ8g+k8ZkHLPJ4j+Uwyk3PMJov/DFEi55xNE/0fBKBhS4NWnz1RE9Gm4Q5fKEAfwt+whZ8tgHAq5\nBjKkDwf4DSEHMYw23EfBKBi+QMo9Hw/6jw1IuefjQf+xASn3fDzo/yhABVLu+XjQf2xAyj0fD/pP\nDSDlno8H/acSkHLPx4P+D2sg5Z6PB/0fBaNgqIFXnz5TEQ2phjtS6xxpuTxuw6l3FuSrT6MN91Ew\nCoYzkHLPx4P+YwNS7vl40H9sQMo9Hw/6PwpQgZR7Ph70HxuQcs/Hg/5TA0i55+NB/6kEpNzz8aD/\nwxpIuefjQf9HwSgYauDVp89URPRpuFMDwS5UQm3Zw1vtaGPwMHHqtd0ZRhvuo2AUDF8g5Z6PB/3H\nBqTc8/Gg/9iAlHs+HvR/FKACKfd8POg/NiDlno8H/acGkHLPx4P+UwlIuefjQf+HNZByz8eD/o+C\nUTDUwKtPn6mIhkzDHev2Vrzr5iGLaqh2BiXDaMN9FIyC4Q6k3PPR0H9CQMo9Hw39JwSk3PPR0P9R\ngBtIueejof+EgJR7Phr6T20g5Z6Phv7TAEi556Oh/yMGSLnno6H/o2AUDE3w6tNnKqKh0nCHtMLR\nhtUhgrib5pClNVQadGcYbbiPglEwCkbBKBgFo2AUjAJSwKtPn6mI6N9wh7a2UQDhtjVk6QvayDpE\nEHm9OyqCrq5B00UmYhhtuI+CUTAKRsEoGAWjYBSMAlLAq0+fqYjo2nCHrWzBAVCXwaAiSBsdrQkO\nEcTdcIcqQNNFJmIYbbiPglEwCkbBKBgFo2AUjAJSwKtPn6mI6NdwR7Ta0RvoiDF4nIteoGqwL5XB\n2XCHjLgTHs4nCjGMNtxHwSgYBaNgFIyCUTAKRgEp4NWnz1RE9Gq4Q9rQeBrZkPXo6E1zBIK0+9Fa\n9hBBjJ4ADEHMxCVLImIYbbiPglEwCkbBKBgFo2AUjAJSwKtPn6mI6NRwx3omDBoipAay7gVNAVQQ\nS38A2lXA2RMgFTGMNtxHwSgYBaNgFIyCUTAKRgEp4NWnz1RE9Gm4E1rTAkGQpjaelS0QBRCAUIal\n7Q4diUcVpBAxjDbcR8EoGAWjYBSMglEwCkYBKeDVp89URPRpuEPa1gQ3iRKjDNoHIAZQsdX+6tNo\nw30UjIJRMApGwSgYBaNgFJAGXn36TEU05BruMIQ8+o4OiDOBRMQw2nAfBaNgFIyCUTAKRsEoGAWk\ngFefPlMR0afhDh0mJzAEDtlLilgDM7gQw2jDfRSMglEwCkbBKBgFo2AUkAJeffpMRUSfhjvBjaef\nXxGnZgARw2jDfRSMglEwCkbBKBgFo2AUkAJeffpMRUSnhjt8ZQvOQXfIcDv1DoGhOmIYbbiPglEw\nCkbBKBgFo2AUjAJSwKtPn6mI6NVwRzrpBWNMHbqQhgHjmPZBhRhGG+6jYBSMglEwCkbBKBgFo4AU\n8OrTZyoi+jXcXyG33bEC9Ab94EIMow33UTAKRsEoGAWjYBSMglFACnj16TMVEV0b7q8+fUYeX0eA\nwbohFRkxjDbcR8EoGAWjYBSMglEwCkYBKeDVp89URPRvuA9VNNpwHwWjYBSMglEwCkbBKBgFJIFX\nnz5TEY023IlFow33UTAKRsEoGAWjYBSMglFAEnj16TMVEZ0b7uviGDDWsq9JZBjc21IhiGG04T4K\nRsEoGAWjYBSMglEwCkgBrz59piKiX8MdsTMVdUU7QnwQnwX56tNow30UjIJRMApGwSgYBaNgFJAG\nXn36TEVEr4b7mkQGCEAbboci+I7VxOWfPr8alIhhtOE+CkbBKBgFo2AUjIJRMApIAa8+faYiolPD\nHXIrKv71MBA1OFr2A48YRhvuo4AQkHLP/08iIEPLKBiEgIx4JFULqerJA4PWFjK0kArIsIIMLfQB\ng9Zho4AkMBqPwwO8+vSZiog+Dfd1cQwMhFfCQG5XRV1IM3gQw2jDfRTgBlLu+WjoPyEg5Z6Phv6P\ngiEIpNzz0dB/QkDKPR8N/ccLpNzz0dB/GgAp93w09J8GQMo9Hw39JwSk3PPR0H9qAyn3fDT0nxCQ\ncs9HQ/8HB5Byz0dD/0fBEARS7vlo6P8oGLLg1afPVET0bLgTXAZDpLKBQQyjDfdRgA1IuefjQf+x\nASn3fDzo/ygYIkDKPR8P+o8NSLnn40H/MYCUez4e9J9KQMo9Hw/6TyUg5Z6PB/3HBqTc8/Gg/9QA\nUu75eNB/bEDKPR8P+j9wQMo9Hw/6PwqGCJByz8eD/o+CIQheffpMRUTPhjtxI+6jDfdRMKSAlHs+\nHvQfG5Byz8eD/o+CIQKk3PPxoP/YgJR7Ph70HwNIuefjQf+pBKTc8/Gg/1QCUu75eNB/bEDKPR8P\n+k8NIOWejwf9xwak3PPxoP8DB6Tc8/Gg/6NgiAAp93w86P8oGILg1afPVET0abhD16/Hrfn86hNu\nBNnAOrpUZhQMKSDlno8H/ccGpNzz8aD/o2CIACn3fDzoPzYg5Z6PB/3HAFLu+XjQfyoBKfd8POg/\nlYCUez4e9B8bkHLPx4P+UwNIuefjQf+xASn3fDzo/8ABKfd8POj/KBgiQMo9Hw/6PwqGIHj16TMV\nEZ0a7tBGOb7RdMioPIENrAOIGEYb7qMAA0i55xNE/1GBlHs+QfR/FAx6IOWeTxD9RwVS7vkE0X8k\nIOWeTxD9pxhIuecTRP8pBlLu+QTRf1Qg5Z5PEP2nDEi55xNE/1GBlHs+QfR/IICUez5B9H8UDHog\n5Z5PEP0fBUMNvPr0mYqIXg13xIGPWJrmiKPcB+tw+6tPow33UYAFSLnnE0T/UYGUez5B9H8UDHog\n5Z5PEP1HBVLu+QTRfyQg5Z5PEP2nGEi55xNE/ykGUu75BNF/VCDlnk8Q/acMSLnnE0T/UYGUez5B\n9H8ggJR7PkH0fxQMeiDlnk8Q/R8FQw28+vSZiohuDffPyG137GAQt9pffRptuI8CLEDKPZ8g+o8K\npNzzCaL/o2DQAyn3fILoPyqQcs8niP4jASn3fILoP8VAyj2fIPpPMZByzyeI/qMCKfd8gug/ZUDK\nPZ8g+o8KpNzzCaL/AwGk3PMJov+jYNADKfd8guj/KBhq4NWnz1RE9Gy4QxDW5jueJTSDBTGMNtxH\nAQaQcs8niP6jAin3fILo/ygY9EDKPZ8g+o8KpNzzCaL/SEDKPZ8g+k8xkHLPJ4j+Uwyk3PMJov+o\nQMo9nyD6TxmQcs8niP6jAin3fILo/0AAKfd8guj/KBj0QMo9nyD6PwqGGnj16TMVEf0b7kMVjTbc\nRwFWIOWejwf9xwak3PPxoP+jYIgAKfd8POg/NiDlno8H/ccAUu75eNB/KgEp93w86D+VgJR7Ph70\nHxuQcs/Hg/5TA0i55+NB/7EBKfd8POj/wAEp93w86P8oGCJAyj0fD/o/CoYgePXpMxXRaMOdWDTa\ncB8FWIGUez4e9B8bkHLPx4P+j4IhAqTc8/Gg/9iAlHs+HvQfA0i55+NB/6kEpNzz8aD/VAJS7vl4\n0H9sQMo9Hw/6Tw0g5Z6PB/3HBqTc8/Gg/wMHpNzz8aD/o2CIACn3fDzo/ygYguDVp89URAPccEds\nSyV4WORAI4bRhvsowAak3PPxoP/YgJR7Ph70fxQMESDlno8H/ccGpNzz8aD/GEDKPR8P+k8lIOWe\njwf9pxKQcs/Hg/5jA1Lu+XjQf2oAKfd8POg/NiDlno8H/R84IOWejwf9HwVDBEi55+NB/0fBEASv\nPn2mIqJrwx3STIc30CFcFJC47tWnz4MTMYw23EcBbiDlno+G/hMCUu75aOj/KBiCQMo9Hw39JwSk\n3PPR0H+8QMo9Hw39pwGQcs9HQ/9pAKTc89HQf0JAyj0fDf2nNpByz0dD/wkBKfd8NPR/cAAp93w0\n9H8UDEEg5Z6Phv6PgiELXn36TEVEt4Y7Yk8q7DhI6MHt0MNkoNemIpr1gw0xjDbcR8EoGAWjYBSM\nglEwCkYBKeDVp89URPRquEMvYDJrPv/51afPrz7BRRAtdegA/GAddGcYbbiPglEwCkbBKBgFo2AU\njAJSwKtPn6mI6NRwX57IwIDURn8FE2FAPggSMugOGYAffIhhtOE+CkbBKBgFo2AUjIJRMApIAa8+\nfaYiok/DHbIqBqmNDl85gzK+jqlsECGG0Yb7KBgFo2AUjIJRMApGwSggBbz69JmKaIAa7pDBdcR6\n98+vPsEFkZQNJsQw2nAfBaNgFIyCUTAKRsEoGAWkgFefPlMRDVDDHWOB+6tPMMHRpTKjYBSMglEw\nCkbBKBgFo2BYgFefPlMR0afhDlkYg9iZimWBO1wQZfHMIEIMow33UTAKRsEoGAWjYBSMglFACnj1\n6TMVEX0a7rATYyCj6dAlMShtdGirHalxP9gQw2jDfRSMglEwCkbBKBgFo2AUkAJeffpMRUSnhjts\n0B0ZwNvokIU0DAxoS94HGWIYbbiPglEwCkbBKBgFo2AUjAJSwKtPn6mI6NZw/4zadkdeJAMRRxYZ\njIhhtOE+CgiB0cvtSAKjwUUSoE9wkWHLcNJCB0CGqwatFlIBHawYBaNgEIJXnz5TEdGz4T600WjD\nfRTgAVLu+Wjo/yjADaTc89HQ/1GAG0i556Oh/zQAUu75aOg/ISDlno+G/hMCUu75aOg/ISDlno+G\n/hMCUu75aOj/IABS7vlo6D8hIOWej4b+EwJS7vlo6D8hIOWej4b+UxtIueejof+jYBSMGPDq02cq\notGGO7FotOE+CrACKfd8POj/KEAFUu75eND/UYAKpNzz8aD/VAJS7vl40H9sQMo9Hw/6jw1Iuefj\nQf+xASn3fDzoPzYg5Z6PB/0fICDlno8H/ccGpNzz8aD/2ICUez4e9B8bkHLPx4P+UwNIuefjQf9H\nwSgYAeDVp89URKMNd2LRaMN9FGAFUu75eND/UYAKpNzz8aD/owAVSLnn40H/qQSk3PPxoP/YgJR7\nPh70HxuQcs/Hg/5jA1Lu+XjQf2xAyj0fD/o/QEDKPR8P+o8NSLnn40H/sQEp93w86D82IOWejwf9\npwaQcs/Hg/6PglEwAsCrT5+piEYb7sSi0Yb7KMAKpNzz8aD/owAVSLnn40H/RwEqkHLPx4P+UwlI\nuefjQf+xASn3fDzoPzYg5Z6PB/3HBqTc8/Gg/9iAlHs+HvR/gICUez4e9B8bkHLPx4P+YwNS7vl4\n0H9sQMo9Hw/6Tw0g5Z6PB/0fBaNgBIBXnz5TEY023IlFow33UYAJpNzzCaL/owAGpNzzCaL/owAG\npNzzCaL/FAMp93yC6D8qkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC6D/dgZR7PkH0HxVIuecT\nRP9RgZR7PkH0HxVIuecTRP8pA1Lu+QTR/1EwCoY7ePXpMxXRaMOdWDTacB8FmEDKPZ8g+j8KYEDK\nPZ8g+j8KYEDKPZ8g+k8xkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC6D8qkHLPJ4j+0x1IuecT\nRP9RgZR7PkH0HxVIuecTRP9RgZR7PkH0nzIg5Z5PEP0fBaNguINXnz5TEY023IlFow33UYAJpNzz\nCaL/owAGpNzzCaL/owAGpNzzCaL/FAMp93yC6D8qkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC\n6D/dgZR7PkH0HxVIuecTRP9RgZR7PkH0HxVIuecTRP8pA1Lu+QTR/1EwCoY7ePXpMxXRaMOdWDTa\ncB8FmEDKPZ8g+j8KYEDKPZ8g+j8KYEDKPZ8g+k8xkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC\n6D8qkHLPJ4j+0x1IuecTRP9RgZR7PkH0HxVIuecTRP9RgZR7PkH0nzIg5Z5PEP0fBaNguINXnz5T\nEY023IlFow33UYAVSLnn40H/RwEqkHLPx4P+jwJUIOWejwf9pxKQcs/Hg/5jA1Lu+XjQf2xAyj0f\nD/qPDUi55+NB/7EBKfd8POj/AAEp93w86D82IOWejwf9xwak3PPxoP/YgJR7Ph70nxpAyj0fD/o/\nCkbBCACvPn2mIhptuBOLRhvuowArkHLPx4P+jwJUIOWejwf9HwWoQMo9Hw/6TyUg5Z6PB/3HBqTc\n8/Gg/9iAlHs+HvQfG5Byz8eD/mMDUu75eND/AQJS7vl40H9sQMo9Hw/6jw1IuefjQf+xASn3fDzo\nPzWAlHs+HvR/FIyCEQBeffpMRTTacCcWjTbcRwFWIOWejwf9HwWoQMo9Hw/6PwpQgZR7Ph70n0pA\nyj0fD/qPDUi55+NB/7EBKfd8POg/NiDlno8H/ccGpNzz8aD/AwSk3PPxoP/YgJR7Ph70HxuQcs/H\ng/5jA1Lu+XjQf2oAKfd8POj/KBgFIwC8+vSZiog+Dfd1cQwkg7g1n199GkSIYbThPgpwAyn3fDT0\nfxTgBlLu+Wjo/yjADaTc89HQfxoAKfd8NPSfEJByz0dD/wkBKfd8NPSfEJByz0dD/wkBKfd8NPR/\nEAAp93w09J8QkHLPR0P/CQEp93w09J8QkHLPR0P/qQ2k3PPR0P9RMApGDHj16TMV0eBtuDMwMDAw\nJC7/9PnV4EAMow33UTAKRsEooDuQcs9HQ/9HwSgYBaNg6IBXnz5TEdGn4f751afPJzvMGCAgcd2r\nT5+R0fJEBihASMHa+madJz99fjUIEMNow30UjIJRMAroCKTc8/Gg/6NgFIyCUTAUwKtPn6mI6NVw\nP99pwsDAgGcBDBYFF5vNGBhQRAYSMYw23EfBKBgFo4COQMo9Hw/6PwpGwSgYBUMBvPr0mYqITg13\nyJi6ScfFV58+40LQIXnEoPvnV2sSGRhQRQYOMYw23EfBKBgFo4COQMo9Hw/6PwpGwSgYBUMBvPr0\nmYqIPg13yLoXs+bzn199wo2gg+5I69ohIoNjtQzDaMN9FIyCUTAK6AWk3PMJov+jYBSMglEw6MGr\nT5+piOjZcEdqkWNHmMowRQYMMYw23EfBKBgFo4BeQMo9nyD6PwpGwSgYBYMevPr0mYqIng134kbc\nUcbXIRpHG+6jYBSMglEwsoCUez5B9H8UjIJRMAoGPXj16TMVEX0a7uSuccfSlB8wxDDacB8Fo2AU\njAJ6ASn3fILo/ygYBaNgFAx68OrTZyoiOjXcodtM8Qy6Q9roqGfIYGnKDxxiGG24j4JRMApGAR2B\nlHs+HvR/FIyCUTAKhgJ49ekzFRG9Gu6wQXcGBiwNcexS2JryA4gYRhvuo2AUjIJRQEcg5Z6PB/0f\nBaNgFIyCoQBeffpMRUS/hvsr+Ag6DoC8kAahErkpP6CIYbThPgpGwSgYBXQEUu75eND/oQmGrstH\nwSgYBeSBV58+UxHRteEOQYhGORxgtM4hY/DITfkBRwyjDfdRMApGwSigO5Byz0dD/4cmkHLPR0P/\nR8EoGAUjALz69JmKaAAa7kMUjTbcR8EoGAWjYBSQAaTc8/Gg/6NgFIyCYQ1effpMRTTacCcWjTbc\nR8EoGAWjYBSQAaTc8/Gg/6NgFIyCYQ1effpMRTTacCcWjTbcR8EoGAWjYBSQAaTc8/Gg/6NgFIyC\nYQ1effpMRUTXhjuW1e1YwKC4bgkTMYw23EfBKBgFo2AUkAik3PMJov+jYBSMguELXn36TEVEt4b7\nxWZiGu0MDKMN91EwCkbBKBgFwwZIuecTRP9HwSgYBcMXvPr0mYqITg13xFg7xgEyQwUxjDbcR8Eo\nGAWjYBSQCKTc8wmi/6NgFIyC4QteffpMRUSfhjtsuN2s8+Snz6+GJmIYbbiPglEwCkbBKCARSLnn\nE0T/R8EoGAXDF7z69JmKiD4N93VxDAwMg+xcdlIRw2jDfRSMglEwCkYB6UDKPR8P+j8KRsEoGNbg\n1afPVER0bbjHrfn86tNQRQyjDfdRMApGwSgYBaQDKfd8POj/KBgFo2BYg1efPlMR0afhDl0qMzri\nPgpGwSigMyCjYTRotZAK6GDF/0FsCxlaSAVEWiHlno8H/R8cgA4uoYMVo2AUDELw6tNnKiL6NNw/\nL09kYGAYwjtTX30abbiPglEwxICUez4a+k8ISLnno6H/hICUez4a+k8ISLnno6H/1AZS7vlo6D8N\ngJR7Phr6TwMg5Z6Phv4TAlLu+WjoP7WBlHs+GvpPCEi556Oh/4MDSLnno6H/1AZS7vlo6P8oGAUj\nBrz69JmKiE4N91fnO00YGBgYzJrPf371aUgihtGG+ygYBUMESLnn40H/sQEp93w86D82IOWejwf9\nxwak3PPxoP/UAFLu+XjQfyoBKfd8POg/lYCUez4e9B8bkHLPx4P+UwNIuefjQf+HFJByz8eD/lMD\nSLnn40H/R8EoGAHg1afPVET0argjnQg5RFe6M4w23EfBKBgiQMo9Hw/6jw1IuefjQf+xASn3fDzo\nPzYg5Z6PB/2nBpByz8eD/lMJSLnn40H/qQSk3PPxoP/YgJR7Ph70nxpAyj0fD/o/pICUez4e9J8a\nQMo9Hw/6PwpGwQgArz59piKiT8MdujmVODB6AdMoGAWjgCIg5Z6PB/3HBqTc8/Gg/9iAlHs+HvQf\nG5Byz8eD/lMDSLnn40H/qQSk3PPxoP9UAlLu+XjQf2xAyj0fD/pPDSDlno8H/R9SQMo9Hw/6Tw0g\n5Z6PB/0fBaNgBIBXnz5TEY023IlFDKMN91EwCoYCkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC\n6D9lQMo9nyD6TzGQcs8niP5TDKTc8wmi/6hAyj2fIPpPGZByzyeI/g8RIOWeTxD9pwxIuecTRP9H\nwSgY7uDVp89URPRpuA8HNNpwHwWjYEgAKfd8gug/KpByzyeI/qMCKfd8gug/KpByzyeI/lMGpNzz\nCaL/FAMp93yC6D/FQMo9nyD6jwqk3PMJov+UASn3fILo/xABUu75BNF/yoCUez5B9H8UjILhDl59\n+kxFNNpwJxaNNtxHwSgYEkDKPZ8g+o8KpNzzCaL/qEDKPZ8g+o8KpNzzCaL/lAEp93yC6D/FQMo9\nnyD6TzGQcs8niP6jAin3fILoP2VAyj2fIPo/RICUez5B9J8yIOWeTxD9HwWjYLiDV58+UxGNNtyJ\nRaMN91EwCoYEkHLPJ4j+owIp93yC6D8qkHLPJ4j+owIp93yC6D9lQMo9nyD6TzGQcs8niP5TDKTc\n8wmi/6hAyj2fIPpPGZByzyeI/g8RIOWeTxD9pwxIuecTRP9HwSgY7uDVp89URKMNd2LRaMN9FIyC\noQKk3PPxoP/YgJR7Ph70HxuQcs/Hg/5jA1Lu+XjQf2oAKfd8POg/lYCUez4e9J9KQMo9Hw/6jw1I\nuefjQf+pAaTc8/Gg/0MKSLnn40H/qQGk3PPxoP+jYBSMAPDq02cqIho13GG7Uc06TyJziQKjm1NH\nwSgYBRQBKfd8POg/NiDlno8H/ccGpNzz8aD/2ICUez4e9J8aQMo9Hw/6TyUg5Z6PB/2nEpByz8eD\n/mMDUu75eNB/agAp93w86P+QAlLu+XjQf2oAKfd8POj/KBgFIwC8+vSZimi04U4sYhhtuI+CUTBE\ngJR7Ph70HxuQcs/Hg/5jA1Lu+XjQf2xAyj0fD/pPDSDlno8H/acSkHLPx4P+UwlIuefjQf+xASn3\nfDzoPzWAlHs+HvR/SAEp93w86D81gJR7Ph70fxSMghEAXn36TEVEo4b7MESjDfdRMAqGFpByz0dD\n/wkBKfd8NPSfEJByz0dD/wkBKfd8NPSf2kDKPR8N/acBkHLPR0P/aQCk3PPR0H9CQMo9Hw39pzaQ\ncs9HQ/+HLJByz0dD/6kNpNzz0dD/UTAKRgx49ekzFdFow51YNNpwHwWjYBSMglEwCkbBKBgFJIFX\nnz5TEY023IlFow33UTAKRsEoGAWjYBSMglFAEnj16TMV0RBtuF9sNmPADWiySp5htOE+CkbBKBgF\no2AUjIJRMApIAa8+faYiolHDnaTdqGgAX7N7eSIDCSBx3atPn6mFGEYb7qNgFIyCUTAKRsEoGAWj\ngBTw6tNnKqIh1HBHHmU3az7/+dUnXAjJduixNlRADKMN91EwCkbBKBgFo2AUjIJRQAp49ekzFRGN\nGu5Y0MkOWLsbYyAcLmXScfHVp8/Y0RroYHvcms+vPhGBzneaMDAw4DeTFMQw2nAfBaNgFIyCUTAK\nRsEoGAWkgFefPlMR0avhDm124x4ph7WzcbXLIYtkSGqFQ/sDVBp0ZxhtuI+CUTAKRsEoGAWjYBSM\nAlLAq0+fqYjo1HAnptkNbWdjjMe/+vQZtvoFd7sfK4J2BnCtvSENMYw23EfBKBgFo2AUjIJRMApG\nASng1afPVET0abgT1+zG186GmIBVCg8iTxd2xDDacB8Fo2AUjIJRMApGwSgYBaSAV58+UxHRs+FO\nsAGNRxlEilDTHw3h6wmQjBhGG+4jDJBxt9+g1UIHQIaryNBCH0CGw8jQQgcwOF31fxA7jA5g1O+j\nYBSMNPDq02cqIvo03CEHwhBqdkPa2diXpENMILDYBg1B195gN5BkxDDacB8xQMo9Hw39JwSk3PPR\n0H9CQMo9Hw39JwSk3PPR0P9BAKTc89HQf0JAyj0fDf0fHEDKPR8N/ScEpNzz0dD/QQCk3PPR0P/B\nAaTc89HQ/xEDpNzz0dD/EQOk3PPR0P9RMApGDHj16TMVEX0a7pSvcYfJEmz9wxGkG0DIUuIRw2jD\nfQQAKfd8POg/NiDlno8H/ccGpNzz8aD/2ICUez4e9H+AgJR7Ph70HxuQcs/Hg/4PHJByz8eD/mMD\nUu75eND/AQJS7vl40P+BA1Lu+XjQ/2ENpNzz8aD/wxpIuefjQf9HwSgYAeDVp89URHRquBM+VYag\nAtigOwMDAwOB1S+QdTUMDAwM1Bpuf/VptOE+IoCUez4e9B8bkHLPx4P+YwNS7vl40H9sQMo9Hw/6\nP0BAyj0fD/qPDUi55+NB/wcOSLnn40H/sQEp93w86P8AASn3fDzo/8ABKfd8POj/sAZS7vl40P9h\nDaTc8/Gg/6NgFIwA8OrTZyoiejXcYYPuDNiGwPFIoSG4SqIAjsF78hDDaMN9BAAp93w86D82IOWe\njwf9xwak3PPxoP/YgJR7Ph70f4CAlHs+HvQfG5Byz8eD/g8ckHLPx4P+YwNS7vl40P8BAlLu+XjQ\n/4EDUu75eND/YQ2k3PPxoP/DGki55+NB/0fBKBgB4NWnz1RE9Gu4vyLU7CbYakdCSGPqWAD+8Xgy\nEcNow324Ayn3fILoPyqQcs8niP6jAin3fILoPyqQcs8niP7THUi55xNE/1GBlHs+QfR/IICUez5B\n9B8VSLnnE0T/6Q6k3PMJov8DAaTc8wmi/8MUSLnnE0T/hymQcs8niP6PglEw3MGrT5+piOjacIcg\nzOY7KU32AUMMDAMQVqOInkjKPZ8gevXpMzKScs8niF59+oyMpNzzCaJXnz4jIyn3fILo1afPdEZS\n7vkE0atPn5GRlHs+QfTq02f6Iyn3fILo1afPyEjKPZ8gevXpM52RlHs+QfTq02f6Iyn3fILo1afP\nwxJJuecTRK8+fR6WSMo9nyB69enzKBpFo4h4NNoYRSAGQuDVp8+jaBgjKfd8gujVp8/ISMo9nyB6\n9ekzMpJyzyeIXn36jIyk3PMJolefPtMZSbnnE0SvPn1GRlLu+QTRq0+f6Y+k3PMJolefPiMjKfd8\ngujVp890RlLu+QTRq0+f6Y+k3PMJolefPg9LJOWeTxC9+vR5WCIp93yC6NWnz6NoFI0i4tFoY5RY\nxDC6VGa4Ayn3fILoPyqQcs8niP6jAin3fILoPyqQcs8niP7THUi55xNE/1GBlHs+QfR/IICUez5B\n9B8VSLnnE0T/6Q6k3PMJov8DAaTc8wmi/8MUSLnnE0T/hymQcs8niP6PglEw3MGrT5+piAZbw31d\nHDkr1JEPnGFgYGCg7rZUCGIYbbiPACDlno8H/ccGpNzz8aD/2ICUez4e9B8bkHLPx4P+DxCQcs/H\ng/5jA1Lu+XjQ/4EDUu75eNB/bEDKPR8P+j9AQMo9Hw/6P3BAyj0fD/o/rIGUez4e9H9YAyn3fDzo\n/ygYBSMAvPr0mYqIng13jOY1doC14b4OshsVy2r4NRhL5mEAi2IKEMNow30EACn3fDzoPzYg5Z6P\nB/3HBqTc8/Gg/9iAlHs+HvR/gICUez4e9B8bkHLPx4P+DxyQcs/Hg/5jA1Lu+XjQ/wECUu75eND/\ngQNS7vl40P9hDaTc8/Gg/8MaSLnn40H/R8EoGAHg1afPVER0a7hDW94EQdyaz68+YSKodvS2OLzV\njjbETu3bl159Gm24jwgg5Z6PB/3HBqTc8/Gg/9iAlHs+HvQfG5Byz8eD/g8QkHLPx4P+YwNS7vl4\n0P+BA1Lu+XjQf2xAyj0fD/o/QEDKPR8P+j9wQMo9Hw/6P6yBlHs+HvR/WAMp93w86P8oGAUjALz6\n9JmKiE4Nd/R7TyENbnhrm3A7G2vDHasgHEEH+HH0BEhGDKMN9xEDpNzz0dB/QkDKPR8N/ScEpNzz\n0dB/QkDKPR8N/R8EQMo9Hw39JwSk3PPR0P/BAaTc89HQf0JAyj0fDf0fBEDKPR8N/R8cQMo9Hw39\nHzFAyj0fDf0fMUDKPR8N/R8Fo2DEgFefPlMR0afhDm1DI7Ww18UxMKBcawptu8Na9uhoXRwDAwOK\nCbDWP04tMAXw7gFliGG04T4KRsEoGAWjYBSMglEwCkgBrz59piKiT8Md0uxGbmFDmvIoy9kho/Io\nTXMEgpiAIgtRj9L6R0OQzgAeBaQghtGG+ygYBaNgFIyCUTAKRsEoIAW8+vSZimigGu6Qa5hQRPC2\nsyEmkNhwh+pC6R6QjRhGG+6jYBSMglEwCkbBKBgFo4AU8OrTZyoiejbcURrQyxMZGNAXoGNRBkMQ\nKRIb7pCeAHYDSUYMow33UTAKRsEoGAWjYBSMglFACnj16TMVET0b7ijj65BmN3JDHKYMazsbIoWq\nnmC7HLLGHU/LnhTEMNpwHwWjYBSMglEwCkbBKBgFpIBXnz5TEdGn4Q5Z0Y46vg5pVaPsHIW0zrE2\nxCFSUAA3B9uwPRxBLUVp61OAGEYb7qNgFIyCUTAKRsEoGAWjgBTw6tNnKiL6NNyh4+sozXTM8XJI\nUx77ADlKwx0DIBkCQVDDcZlGDmIYbbiPglEwCkbBKBgFo2AUjAJSwKtPn6mI6NRwR7S8EW13tBFx\nqAIYFzuCdgAwAJIuqLEMDAwMmA16ChDDaMN9FIyCUTAKRsEoGAWjYBSQAl59+kxFRLeGO2xAnYEB\n0XaHj4sjAIlNbZiZyKtlIOtnkJry1EEMow33UTAKRsEoGAWjYBSMglFACnj16TMVER0b7p8+Q4bV\nUZvU6+IYYIB6y1pogRhGG+6jYBSMgoEDo5dNjoJRMApGwVAErz59piKic8N9CKPRhvsoGAWjYECA\nlHs+Gvo/CkbBKBgFo2CIgFefPlMRjTbciUWjDfdRMApGAZ2BlHs+HvR/FIyCUTAKRsGgB68+faYi\nGoCGO2QNOgMDAwPsZPeTHWaIhe+DFTGMNtxHwSgYBfQFUu75eND/UTAKRsEoGAWDHrz69JmKiK4N\n95MdiANfGBgY4A13WFOexJ2p9EUMow33UTAKRgF9gZR7Ph70fxSMglEwCkbBoAevPn2mIqJfwx3e\nao9b8xm2JxXacIdxGQbz/lSG0Yb7KBgFo4COQMo9nyD6PwpGwSgYBaNgcINXnz5TEdGr4Q49+RE+\npg5pqcMb7p9hImhnzgwixDDacB8Fo2AU0BFIuecTRP9HwSgYBaNgFAxu8OrTZyoiOjXcIYthkBrl\nkGY6csMdNiQ/WBe7M4w23EfBKBgFdARS7vkE0f9RMApGwSgYBYMbvPr0mYqIPg13yG2myM10LA13\njFH5wYUYRhvuo2AUjAI6Ain3fILo/ygYBaNgFIyCwQ1effpMRUSfhjukmY7cIoeIoDbcoYLIygYR\nYhhtuI+CUTAK6Auk3PPxoP+jYBSMglEwCgY9ePXpMxXRYGq4j464j4JRMApGARKQcs/Hg/6PglEw\nCkbBKBj04NWnz1RE9Gm4E7dUZk0iAwPDoD1YhmG04T7CAH0aRmTYMji1kKr+//DSQgYgxhYp93w8\n6D8hQIwaNECGFjIAGbaQoYUOgAxXkaGFDEAfW0bBKBgFxIBXnz5TEdGn4U7U5lSImtHNqaNgwIGU\nez4a+k8DIOWej4b+EwJS7vlo6D8hIOWej4b+EwJS7vlo6D9eIOWej4b+EwJS7vlo6D8hIOWej4b+\nEwJS7vlo6D8hIOWej4b+0wBIueejof94gZR7Phr6TwhIueejof+EgJR7Phr6TwMg5Z6Phv4TAlLu\n+Wjo/yAAUu75aOg/ISDlno+G/tMASLnno6H/o2AUjIIBBa8+faYiolPDHbYMBt5SR2+4Q4+UQRIZ\nbIhhtOE+AoCUez4e9J9KQMo9Hw/6jw1IuefjQf+xASn3fDzoPzYg5Z6PB/3HAFLu+XjQf2xAyj0f\nD/qPDUi55+NB/7EBKfd8POg/NiDlno8H/acSkHLPx4P+YwAp93w86D82IOWejwf9xwak3PPxoP9U\nAlLu+XjQf2xAyj0fD/o/QEDKPR8P+o8NSLnn40H/qQSk3PPxoP+jYBSMggECrz59piKiV8Md0TTH\nvIAJspCGgQFlSH7QIYbRhvsIAFLu+XjQfyoBKfd8POg/NiDlno8H/ccGpNzz8aD/2ICUez4e9B8D\nSLnn40H/sQEp93w86D82IOWejwf9xwak3PPxoP/YgJR7Ph70n0pAyj0fD/qPAaTc8/Gg/9iAlHs+\nHvQfG5Byz8eD/lMJSLnn40H/sQEp93w86P8AASn3fDzoPzYg5Z6PB/2nEpByz8eD/o+CUTAKBgi8\n+vSZioh+DfdXSG13rGAwt9pffRptuI8IIOWejwf9pxKQcs/Hg/5jA1Lu+XjQf2xAyj0fD/qPDUi5\n5+NB/zGAlHs+HvQfG5Byz8eD/mMDUu75eNB/bEDKPR8P+o8NSLnn40H/qQSk3PPxoP8YQMo9Hw/6\njw1IuefjQf+xASn3fDzoP5WAlHs+HvQfG5Byz8eD/g8QkHLPx4P+YwNS7vl40H8qASn3fDzo/ygY\nBaNggMCrT5+piOjacIcg6Fp2ZDBYN6QiI4bRhvtwB1Lu+QTRf4qBlHs+QfQfFUi55xNE/1GBlHs+\nQfQfFUi55xNE/5GAlHs+QfQfFUi55xNE/1GBlHs+QfQfFUi55xNE/1GBlHs+QfSfYiDlnk8Q/UcC\nUu75BNF/VCDlnk8Q/UcFUu75BNF/ioGUez5B9B8VSLnnE0T/6Q6k3PMJov+oQMo9nyD6TzGQcs8n\niP6PglEwCgYCvPr0mYpoABruQxSNNtyHPZByzyeI/lMMpNzzCaL/qEDKPZ8g+o8KpNzzCaL/qEDK\nPZ8g+o8EpNzzCaL/qEDKPZ8g+o8KpNzzCaL/qEDKPZ8g+o8KpNzzCaL/FAMp93yC6D8SkHLPJ4j+\nowIp93yC6D8qkHLPJ4j+Uwyk3PMJov+oQMo9nyD6T3cg5Z5PEP1HBVLu+QTRf4qBlHs+QfR/FIyC\nUTAQ4NWnz1REdGq4L09kYBisB7QTiRhGG+7DHUi55xNE/ykGUu75BNF/VCDlnk8Q/UcFUu75BNF/\nVCDlnk8Q/UcCUu75BNF/VCDlnk8Q/UcFUu75BNF/VCDlnk8Q/UcFUu75BNF/ioGUez5B9B8JSLnn\nE0T/UYGUez5B9B8VSLnnE0T/KQZS7vkE0X9UIOWeTxD9pzuQcs8niP6jAin3fILoP8VAyj2fIPo/\nCkbBKBgI8OrTZyoi+jTcIVtRB/sqdvyIYbThPtyBlHs+QfSfYiDlnk8Q/UcFUu75BNF/VCDlnk8Q\n/UcFUu75BNF/JCDlnk8Q/UcFUu75BNF/VCDlnk8Q/UcFUu75BNF/VCDlnk8Q/acYSLnnE0T/kYCU\nez5B9B8VSLnnE0T/UYGUez5B9J9iIOWeTxD9RwVS7vkE0X+6Ayn3fILoPyqQcs8niP5TDKTc8wmi\n/6NgFIyCgQCvPn2mIqJrwz1uzedXn4YqYhhtuI8AIOWejwf9pxKQcs/Hg/5jA1Lu+XjQf2xAyj0f\nD/qPDUi55+NB/zGAlHs+HvQfG5Byz8eD/mMDUu75eNB/bEDKPR8P+o8NSLnn40H/qQSk3PPxoP8Y\nQMo9Hw/6jw1IuefjQf+xASn3fDzoP5WAlHs+HvQfG5Byz8eD/g8QkHLPx4P+YwNS7vl40H8qASn3\nfDzo/ygYBaNggMCrT5+piOjTcIcslWEYtJcrEYMYRhvuIwBIuefjQf+pBKTc8/Gg/9iAlHs+HvQf\nG5Byz8eD/mMDUu75eNB/DCDlno8H/ccGpNzz8aD/2ICUez4e9B8bkHLPx4P+YwNS7vl40H8qASn3\nfDzoPwaQcs/Hg/5jA1Lu+XjQf2xAyj0fD/pPJSDlno8H/ccGpNzz8aD/AwSk3PPxoP/YgJR7Ph70\nn0pAyj0fD/o/CkbBKBgg8OrTZyoiOjXcXyHOghyqK90ZRhvuIwBIuefjQf+pBKTc8/Gg/9iAlHs+\nHvQfG5Byz8eD/mMDUu75eNB/DCDlno8H/ccGpNzz8aD/2ICUez4e9B8bkHLPx4P+YwNS7vl40H8q\nASn3fDzoPwaQcs/Hg/5jA1Lu+XjQf2xAyj0fD/pPJSDlno8H/ccGpNzz8aD/AwSk3PPxoP/YgJR7\nPh70n0pAyj0fD/o/CkbBKBgg8OrTZyoi+jTcoUtliAODtGXPMNpwHzFAyj0fDf2nAZByz0dD/wkB\nKfd8NPSfEJByz0dD/wkBKfd8NPQfL5Byz0dD/wkBKfd8NPSfEJByz0dD/wkBKfd8NPSfEJByz0dD\n/2kApNzz0dB/vEDKPR8N/ScEpNzz0dB/QkDKPR8N/ScOEK/y////Uu75aOg/ISDlno+G/g8CIOWe\nj4b+EwJS7vlo6D8NgJR7Phr6PwpGwSgYUPDq02cqotGGO7GIYbThPgpGwSgYBUhAyj0fDf0fBaNg\nFIyCUYAKXn36TEVEn4b7cECjDfdRMApGwSiAACn3fDzo/ygYBaNgFIwCGHj16TMV0WjDnVg02nAf\nBaNgFIwCCJByz8eD/o+CUTAKRsEogIFXnz5TEY023IlFow33UTAKRsEogAAp93w86P8oGAWjYBSM\nAhh49ekzFdFow51YNNpwHwWjYBSMgv+EWu0Q9H8UjIJRMApGwf///0cb7gOFRhvuo2AUjIJR8H+0\n4T4KRsEoGAWkgFefPlMRjTbciUWjDfdRMApGwSj4P9pwHwWjYBSMAlLAq0+fqYhGG+7EotGG+ygY\nBaNgFPwfbbiPglEwCkYBKeDVp89URKMNd2LRaMN9FIyCUTAKIEDKPR8P+j8KRsEoGAWjAAZeffpM\nRTTacCcWjTbcR8EoGAWjAAKk3PPxoP+jYBSMglEwCmDg1afPVESjDXdi0WjDfRSMKDDa/CIJjLTg\nknLPx4P+j4JRMApGwSiAgVefPlMRjTbciUWjDfdRMEKAlHs+Gvo/CnADKfd8NPR/xAAp93w09H8U\njIJRMApGASp49ekzFdFow51YNNpwHwXDHki55+NB/0cBKpByz8eD/o+CUTAKRsEoGAWjDfeBQqMN\n91Ew7IGUez4e9H8UoAIp93w86P8oGAWjYBSMglEw2nAfKDTacB8Fwx5IuefjQf9HASqQcs/Hg/6P\nglEwCkbBKBgFow33gUKjDfdRMLyBlHs+QfR/FMCAlHs+QfR/FIyCUTAKRsGIB68+faYiGm24E4tG\nG+6jYHgDKfd8guj/KIABKfd8guj/KBgFo2AUjIIRD159+kxFNNpwJxaNNtxHwfAGUu75BNH/UQAD\nUu75BNH/UTAKRsEoGAUjHrz69JmKaLThTiwabbiPguENpNzzCaL/owAGpNzzCaL/o2AUjIJRMApG\nPHj16TMV0WjDnVg02nAfBcMeSLnn40H/RwEqkHLPx4P+j4JRMApGwSgYBaMN94FCow33UTDsgZR7\nPh70fxSgAin3fDzo/ygYBaNgFIyCUTDacB8oNNpwH2mAjLYXfbSQAYi0Rco9Hw/6T21ACzMxARm2\nEKlFyj0fD/pPCBCjhv5gcLpqOIHREB4Fo2CkgVefPlMRjTbciUWjDfeRA6Tc89HQf0JAyj0fDf0n\nBKTc89HQfxoAKfd8NPSfEJByz0dD/6kNpNzz0dB/GgAp93w09J8QkHLPR0P/CQEp93w09J8QkHLP\nR0P/BwGQcs9HQ/9HAVWBlHs+Gvo/CkbBKBgB4NWnz1REow13YtFow30kACn3fDzoPzYg5Z6PB/3H\nBqTc8/Gg/1QCUu75eNB/bEDKPR8P+k8NIOWejwf9pxKQcs/Hg/5jA1Lu+XjQfyoBKfd8POj/AAEp\n93w86P8ooBhIuefjQf9HwSgYBcMavPr0mYpotOFOLBptuI8EIOWejwf9xwak3PPxoP/YgJR7Ph70\nn0pAyj0fD/qPDUi55+NB/6kBpNzz8aD/VAJS7vl40H9sQMo9Hw/6TyUg5Z6PB/0fICDlno8H/R8F\nFAMp93w86P8oGAWjYFiDV58+UxGNNtyJRaMN95EApNzz8aD/2ICUez4e9B8bkHLPx4P+UwlIuefj\nQf+xASn3fDzoPzWAlHs+HvSfSkDKPR8P+o8NSLnn40H/qQSk3PPxoP8DBKTc8/Gg/6OAYiDlno8H\n/R8Fo2AUDGvw6tNnKqLRhjuxaLThPuyBlHs+QfQfFUi55xNE/1GBlHs+QfSfYiDlnk8Q/UcFUu75\nBNF/yoCUez5B9J9iIOWeTxD9RwVS7vkE0X+KgZR7PkH0n+5Ayj2fIPo/CigAUu75BNH/UTAKRsHw\nBa8+faYiGm24E4tGG+7DHki55xNE/1GBlHs+QfQfFUi55xNE/ykGUu75BNF/VCDlnk8Q/acMSLnn\nE0T/KQZS7vkE0X9UIOWeTxD9pxhIuecTRP/pDqTc8wmi/6OAAiDlnk8Q/R8Fo2AUDF/w6tNnKqLR\nhjuxaLThPuyBlHs+QfQfFUi55xNE/1GBlHs+QfSfYiDlnk8Q/UcFUu75BNF/yoCUez5B9J9iIOWe\nTxD9RwVS7vkE0X+KgZR7PkH0n+5Ayj2fIPo/CigAUu75BNH/UTAKRsHwBa8+faYiGm24E4tGG+7D\nHki55xNE/1GBlHs+QfQfFUi55xNE/ykGUu75BNF/VCDlnk8Q/acMSLnnE0T/KQZS7vkE0X9UIOWe\nTxD9pxhIuecTRP/pDqTc8wmi/6OAAiDlnk8Q/R8Fo2AUDF/w6tNnKqLRhjuxaLThPhKAlHs+HvQf\nG5Byz8eD/mMDUu75eNB/KgEp93w86D82IOWejwf9pwaQcs/Hg/5TCUi55+NB/7EBKfd8POg/lYCU\nez4e9H+AgJR7Ph70fxRQDKTc8/Gg/6NgFIyCYQ1effpMRTTacCcWjTbcRwKQcs/Hg/5jA1Lu+XjQ\nf2xAyj0fD/pPJSDlno8H/ccGpNzz8aD/1ABS7vl40H8qASn3fDzoPzYg5Z6PB/2nEpByz8eD/g8Q\nkHLPx4P+jwKKgZR7Ph70fxSMglEwrMGrT5+piEYb7sSi0Yb7SABS7vl40H9sQMo9Hw/6jw1Iuefj\nQf+pBKTc8/Gg/9iAlHs+HvSfGkDKPR8P+k8lIOWejwf9xwak3PPxoP9UAlLu+XjQ/wECUu75eNB/\nQoAYNSMcSLnn40H/R8EoGAXDGrz69JmKaLThTiwabbiPHCDlno+G/hMCUu75aOg/ISDlno+G/tMA\nSLnno6H/hICUez4a+k9tIOWej4b+0wBIueejof+EgJR7Phr6TwMg5Z6Phv4PAiDlno+G/hMCUu75\naOj/KMANpNzz0dD/UTAKRsEIAK8+faYiGm24E4tGG+6jACuQcs/Hg/6PglEwHIGUez4e9H8UjIJR\nMApGAQy8+vSZimi04U4sGm24jwKsQMo9Hw/6PwpGwXAEUu75eND/UTAKRsEoGAUw8OrTZyqi0YY7\nsWi04T4KsAIp93w86P8oGAXDEUi55+NB/0fBKBgFo2AUwMCrT5+piEYb7sSi0Yb7KMAEUu75BNH/\nUTAKhheQcs8niP6PglEwCkbBKPj///9ow32g0GjDfRRgAin3fILo/ygYBcMLSLnnE0T/R8EoGAWj\nYBT8//9/tOE+UGi04T4KMIGUez5B9H8UjILhBaTc8wmi/6NgFIyCUTAK/v//P9pwHyg02nAfBZhA\nyj2fIPo/CkbB8AJS7vkE0f9RMApGwSgYBf///x9tuA8UGm24jwKsQMo9Hw/6PwpGwXAEUu75eND/\nUTAKRsEoGAUw8OrTZyqi0YY7sWi04T4KsAIp93w86P8oGAXDEUi55+NB/0fBKBgFo2AUwMCrT5+p\niEYb7sSi0Yb7kAZkNCaI1CLlno8H/ScEiFGDBuijhVRAByv+08sWMgAZDiNDy+ABUu75eNB/GgAa\nGTsKRsEoGAW0Bq8+faYiGm24E4tGG+5DFEi556Oh/4SAlHs+GvpPCEi556Oh/4SAlHs+GvpPCEi5\n56Oh/4SAlHs+GvpPbSDlno+G/tMASLnno6H/gwNIueejof+EgJR7Phr6PzSBlHs+GvpPAyDlno+G\n/o+CUTAKRsHQAa8+faYiGm24E4tGG+5DDki55+NB/7EBKfd8POg/lYCUez4e9B8bkHLPx4P+YwNS\n7vl40H9qACn3fDzoP5WAlHs+HvR/4ICUez4e9B8bkHLPx4P+jwJUIOWejwf9HwWjYBSMgqEAXn36\nTEU02nAnFo023IcckHLPx4P+YwNS7vl40H8qASn3fDzoPzYg5Z6PB/3HBqTc8/Gg/9QAUu75eNB/\nKgEp93w86P/AASn3fDzoPzYg5Z6PB/0fBahAyj0fD/o/CkbBKBgFQwG8+vSZimi04U4sGm24Dzkg\n5Z6PB/3HBqTc8/Gg/1QCUu75eNB/bEDKPR8P+o8NSLnn40H/qQGk3PPxoP9UAlLu+XjQ/4EDUu75\neNB/bEDKPR8P+j8KUIGUez4e9H8UjIJRMAqGAnj16TMV0WjDnVg02nAfWkDKPZ8g+o8KpNzzCaL/\nFAMp93yC6D8qkHLPJ4j+owIp93yC6D9lQMo9nyD6TzGQcs8niP4PBJByzyeI/qMCKfd8guj/KIAB\nKfd8guj/KBgFo2AUDHrw6tNnKqLRhjuxaLThPrSAlHs+QfQfFUi55xNE/ykGUu75BNF/VCDlnk8Q\n/UcFUu75BNF/yoCUez5B9J9iIOWeTxD9Hwgg5Z5PEP1HBVLu+QTR/1EAA1Lu+QTR/1EwCkbBKBj0\n4NWnz1REow13YtFow31oASn3fILoPyqQcs8niP5TDKTc8wmi/6hAyj2fIPqPCqTc8wmi/5QBKfd8\ngug/xUDKPZ8g+j8QQMo9nyD6jwqk3PMJov+jAAak3PMJov+jYBSMglEw6MGrT5+piEYb7sSi0Yb7\n0AJS7vkE0X9UIOWeTxD9pxhIuecTRP9RgZR7PkH0HxVIuecTRP8pA1Lu+QTRf4qBlHs+QfR/IICU\nez5B9B8VSLnnE0T/RwEMSLnnE0T/R8EoGAWjYNCDV58+UxGNNtyJRaMN9yEHpNzz8aD/2ICUez4e\n9J9KQMo9Hw/6jw1IuefjQf+xASn3fDzoPzWAlHs+HvSfSkDKPR8P+j9wQMo9Hw/6jw1IuefjQf9H\nASqQcs/Hg/6PglEwCkbBUACvPn2mIhptuBOLRhvuQw5IuefjQf+xASn3fDzoP5WAlHs+HvQfG5By\nz8eD/mMDUu75eNB/agAp93w86D+VgJR7Ph70f+CAlHs+HvQfG5Byz8eD/o8CVCDlno8H/R8Fo2AU\njIKhAF59+kxFNNpwJxaNNtyHHJByz8eD/mMDUu75eNB/QoAYNf///5dyz8eD/mMDUu75eNB/bEDK\nPR8P+k8NIOWejwf9pxKQcs/Hg/4PHJByz8eD/mMDUu75eND/UYAKpNzz8aD/o2AUjIJRMBTAq0+f\nqYhGG+7EotGG+xAFUu75aOg/ISDlno+G/hMCUu75aOg/ISDlno+G/hMCUu75aOg/ISDlno+G/lMb\nSLnno6H/NABS7vlo6P/gAFLu+WjoPyEg5Z6Phv6PAtxAyj0fDf0fBaNgFIyCoQNeffpMRTTacCcW\njTbcRwFWIOWejwf9pxKQcs/Hg/6PglEwCkbBKBgFo2BQglefPlMRjTbciUWjDfdRgBVIuefjQf+p\nBKTc8/Gg/6NgFIyCUTAKRsEoGJTg1afPVERDtOF+sdmMATdIXP7p8ytqI4bRhvsowAak3PPxoP9U\nAlLu+XjQ/1EwCkbBKBgFo2AUDErw6tNnKqIh1nBfnshAAkhc9+rTZ2ohhtGG+yjAAFLu+QTRf4qB\nlHs+QfR/FIyCUTAKRsEoGAWDD7z69JmKaAg13JFH2c2az39+9QkXWhfHAANmnSc/fX5FDcQw2nAf\nBRhAyj2fIPpPMZByzyeI/o+CUTAKRsEoGAWjYPCBV58+UxENnYb7Guhge9yaz68+EYHOd5owMDAw\nMJh0XHz16TPliGG04T4KMICUez5B9J9iIOWeTxD9HwWjYBSMglEwCkbB4AOvPn2mIhoyDXfIIhmS\nWuEnO8wYGKg26M4w2nAfBRhAyj2fIPpPMZByzyeI/o+CUTAKRsEoGAWjYPCBV58+UxENlYY7ZPUL\n/hUyGAg66E6dvaoMow33UYANSLnn40H/qQSk3PPxoP+jYBSMglEwCkbBKBiU4NWnz1REQ6vhTmoT\nnDxd2BHDaMN9FGADUu75eNB/KgEp93w86P8oGAWjYBSMglEwCgYlePXpMxXR0C/TPF4AAC3WSURB\nVGq4j464j4JBB6Tc8/Gg/1QCUu75eND/UTAKRsEoGAWjYBQMSvDq02cqoqHScIceKTO6xn0UDE4g\n5Z6Phv7TAEi556Oh/6NgFIyCUTAKRsEoGMTg1afPVERDpeEOa4UTP+gOHW4nra2PBzGMNtxHwSgY\nBaNgFIyCUTAKRgEp4NWnz1REQ6bhDh90Z2BgILT6BbKuhoGBgYFaw+2vPo023EfBKBgFo2AUjIJR\nMApGAWng1afPVERDqOH++RXsUEhiwejNqaNgFIyCUTAKRsEoGAWjYODAq0+fqYiGWMMdhpDG1LEA\n/OPxZCKG0Yb7KBgFo2AUjIJRMApGwSggBbz69JmKaIg23AcAMTCMhtUoGkWjaBSNolE0ikbRKBow\nNNoYRSAGQuDVp8+jaBSNolE0ikbRKBpFo2gUDQgaXo3RNZAl8ESfPEMKYhhdKjMKRsEoGAWjYBSM\nglEwCkgBrz59piIabbgTixhGG+6jYBSMglEwCkbBKBgFo4AU8OrTZyqiodJwx78bFT+gzl5VhtGG\n+ygYBaNgFIyCUTAKRsEoIAW8+vSZimi04U4sYhhtuI+CUTAKRsEoGAWjYBSMAlLAq0+fqYiGSsMd\nvgwG702oUDWjS2WwAyn3/P8jFZDhd/poGbRgOPlleAD6xAh9bBkFo2AUjIKRA159+kxFNHQa7p8+\nvzrfacLAwMDAgPNmpdGGOw4g5Z6Phv6PGCDlno+G/hMCUu75aOg/ISDlno+G/g9ZIOWej4b+j4IB\nBVLu+WjoPw2AlHs+Gvo/CkbBKBgFo4Bi8OrTZyqiIdVw//T51aeLzWYMDAwMDFgXwIw23DGAlHs+\nHvR/WAMp93w86D82IOWejwf9xwak3PPxoP9DCki55+NB/0cB3YGUez4e9J9KQMo9Hw/6PwpGwSgY\nBaOAAvDq02cqoiHXcP/86tPnkx3Qxnvcms+vPiGh0YY7BpByz8eD/g9rIOWejwf9xwak3PPxoP/Y\ngJR7Ph70f0gBKfd8POj/KKA7kHLPx4P+UwlIuefjQf9HwSgYBaNgFFAAXn36TEU0JBvurz7B2+gM\nKMtmoIKjDXcEkHLPx4P+D2sg5Z6PB/3HBqTc8/Gg/9iAlHs+HvR/SAEp93w86P8ooDuQcs/Hg/5T\nCUi55+NB/0fBKBgFo2AUUABeffpMRTRkG+6fPiOOmjHrPPnp86tPow13dCDlnk8Q/R+mQMo9nyD6\njwqk3PMJov+oQMo9nyD6P0SAlHs+QfR/FNARSLnnE0T/KQZS7vkE0f9RMApGwSgYBeSCV58+UxEN\n6Yb7Z6Ql72bN50cb7uhAyj2fIPo/TIGUez5B9B8VSLnnE0T/UYGUez5B9H+IACn3fILo/yigI5By\nzyeI/lMMpNzzCaL/o2AUjIJRMArIBa8+faYiGuoN98+vkJa8m5hBGKMNdyiQcs8niP4PUyDlnk8Q\n/UcFUu75BNF/VCDlnk8Q/R8iQMo9nyD6PwroCKTc8wmi/xQDKfd8guj/KBgFo2AUjAJywatPn6mI\nhkPD/dUnpJMiGRhGG+5wIOWeTxD9H6ZAyj2fIPqPCqTc8wmi/6hAyj2fIPo/RICUez5B9H8U0BFI\nuecTRP8pBlLu+QTR/1EwCkbBKBgF5IJXnz5TEQ2Xhvunz4gl76MNdyQg5Z6PB/0f1kDKPR8P+o8N\nSLnn40H/sQEp93w86P+QAlLu+XjQ/1FAdyDlno8H/acSkHLPx4P+j4JRMApGwSigALz69JmKaDg1\n3GmLRhvuQw5IuefjQf+xASn3fDzoPzYg5Z6PB/0fUkDKPR8P+j8K6A6k3PPxoP9UAlLu+XjQ/1Ew\nCkbBKBgFFIBXnz5TEY023IlFow33IQek3PPxoP/YgJR7Ph70HxuQcs/Hg/4PKSDlno8H/R8FdAdS\n7vl40H8qASn3fDzo/ygYBaNgFIwCCsCrT5+piEYb7sSiIdpwhwAp93w09H/EACn3fDT0nxCQcs9H\nQ/8JASn3fDT0f8gCKfd8NPR/FAwokHLPR0P/aQCk3PPR0P9RMApGwSgYBRSDV58+UxGNNtyJRUO6\n4T4KRsEoGAWjYBSMglEwCugPXn36TEU02nAnFo023EfBKBgFo2AUjIJRMApGAUng1afPVESjDXdi\n0WjDfRSMglEwCkbBKBgFo2AUkAReffpMRTTacCcWjTbcR8EoGAWjYBSMglEwCkYBSeDVp89URKMN\nd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59+kxFNNpwJxaNNtxHwSgYBaNgFIyCUTAKRgFJ4NWnz1RE\now13YtFow30UjIJRMApGwSgYBaNgFJAEXn36TEU02nAnFo023EfBKBgFo2AUjIJRMApGAUng1afP\nVESjDXdi0WjDfRSMglEwCkbBKBgFo2AUkAReffpMRTTacCcWjTbcR8EgAYPzSsvB6apRMApGwSgY\nBaNgYMGrT5+piEYb7sSi0Yb7KBhwIOWej4b+DwIg5Z6Phv6PglEwCkbBKBgFo+D///+jDfeBQqMN\n91EwgEDKPR8P+j9AQMo9Hw/6PwpGwSgYBaNgFIx48OrTZyqi0YY7sWi04T4KBhBIuefjQf8HCEi5\n5+NB/0fBKBgFo2AUjIIRD159+kxFNNpwJxaNNtxHwQACKfd8POj/AAEp93w86P8oGAWjYBSMglEw\n4sGrT5+piEYb7sSi0Yb7KBgoIOWeTxD9pzuQcs8niP6PglEwCkbBKBgFIxu8+vSZimi04U4sGm24\nj4KBAlLu+QTRf7oDKfd8guj/KBgFo2AUjIJRMLLBq0+fqYhGG+7EotGG+ygYKCDlnk8Q/ac7kHLP\nJ4j+j4JRMApGwSgYBSMbvPr0mYpotOFOLBptuI+CgQJS7vkE0X+6Ayn3fILo/ygYBaNgFIyCUTCy\nwatPn6mIRhvuxKLRhvsoGEAg5Z6PB/0fICDlno8H/R8Fo2AUjIJRMApGPHj16TMV0WjDnVg02nAf\nBQMIpNzz8aD/AwSk3PPxoP+jYBSMglEwCkbBiAevPn2mIhptuBOLRhvugwqMtHahlHs+HvR/gICU\nez4e9H8UDEEwGnGjYBSMglFAXfDq02cqotGGO7FotOE+SICUez4a+j9igJR7Phr6PwiAlHs+Gvo/\nCoYgkHLPR0P/R8EoGAWjYBRQDF59+kxFNNpwJxaNNtwHHEi55+NB/4c1kHLPx4P+j4JRQAGQcs/H\ng/6PglEwCkbBKKAAvPr0mYpotOFOLBptuA84kHLPx4P+D2sg5Z6PB/0fBaOAAiDlno8H/R8Fo2AU\njIJRQAF49ekzFdFow51YNNpwH3Ag5Z6PB/0f1kDKPR8P+j8KRgEFQMo9Hw/6PwpGwSgYBaOAAvDq\n02cqotGGO7FotOE+sEDKPZ8g+j9MgZR7PkH0fxSMArKAlHs+QfR/FIyCUTAKRgG54NWnz1REow13\nYtFow31ggZR7PkH0f5gCKfd8guj/KBgFZAEp93yC6P8oGAWjYBSMAnLBq0+fqYhGG+7EotGG+8AC\nKfd8guj/MAVS7vkE0f9RMArIAlLu+QTR/1EwCkbBKBgF5IJXnz5TEY023IlFow33gQVS7vkE0f9h\nCqTc8wmi/6NgFJAFpNzzCaL/o2AUjIJRMArIBa8+faYiGm24E4tGG+4DDqTc8/Gg/8MaSLnn40H/\nR8EooABIuefjQf9HwSgYBaNgFFAAXn36TEU02nAnFo023AccSLnn40H/hzWQcs/Hg/6PglFAAZBy\nz8eD/o+CUTAKRsEooAC8+vSZimi04U4sGm24DziQcs/Hg/4PAkA7Z0i55+NB/0fBKKAASLnn40H/\nR8EoGAWjYBRQAF59+kxFNNpwJxaNNtwHCZByz0dD/wcBkHLPR0P/aQCk3PPR0P9RMAqoBKTc89HQ\n/1EwCkbBKBgFFINXnz5TEY023IlFow33UYAVSLnn40H/R8EoGAWjYBSMglEwgsGrT5+piEYb7sSi\n0Yb7KMAKpNzz8aD/o2AUjIJRMApGwSgYweDVp89URKMNd2LRaMN9FGAFUu75eND/UTAKRsEoGAWj\nYBSMYPDq02cqotGGO7FotOE+CjCBlHs+QfR/FIyCUTAKRsEoGAUjFbz69JmKaLThTiwabbiPAkwg\n5Z5PEP0fBaNgFIyCUTAKRsFIBa8+faYiGm24E4tGG+6jABNIuecTRP9HwSgYBaNgFIyCUTBSwatP\nn6mIRhvuxKLRhvsowARS7vkE0f9RMApGwSgYBaNgFIxU8OrTZyqi0YY7sWi04T4KsAIp93w86P8o\nGAWjYBSMglEwCkYwePXpMxXRaMOdWDTacB8FWIGUez4e9H8UjIJRMApGwSgYBSMYvPr0mYpotOFO\nLBptuI80QGSzW8o9Hw/6TwgQo2ZIADI8QoaWUTAKRsEoGAWjYGiBV58+UxGNNtyJRaMN95EDpNzz\n0dB/QkDKPR8N/ScEpNzz0dD/oQmk3PPR0H9CQMo9Hw39HwWjYBSMglEwCoYjePXpMxXRaMOdWDTa\ncB8JQMo9Hw/6jw1IuefjQf+xASn3fDzo/9ABUu75eNB/bEDKPR8P+j8KRsEoGAWjYBQML/Dq02cq\notGGO7FotOE+EoCUez4e9B8bkHLPx4P+YwNS7vl40P+hA6Tc8/Gg/9iAlHs+HvR/FIyCUTAKRsEo\nGF7g1afPVESjDXdi0WjDfSQAKfd8POg/NiDlno8H/ccGpNzz8aD/QwdIuefjQf+xASn3fDzo/ygY\nBaNgFIyCUTC8wKtPn6mIRhvuxKLRhvuwB1Lu+QTRf1Qg5Z5PEP1HBVLu+QTR/6EApNzzCaL/qEDK\nPZ8g+j8KRsEoGAWjYBQMI/Dq02cqotGGO7FotOE+7IGUez5B9B8VSLnnE0T/UYGUez5B9H8oACn3\nfILoPyqQcs8niP6PglEwCkbBKBgFwwi8+vSZimi04U4sGm24D3sg5Z5PEP1HBVLu+QTRf1Qg5Z5P\nEP0fCkDKPZ8g+o8KpNzzCaL/o2AUjIJRMApGwTACrz59piIabbgTi0Yb7sMeSLnnE0T/UYGUez5B\n9B8VSLnnE0T/hwKQcs8niP6jAin3fILo/ygYBaNgFIyCUTCMwKtPn6mIRhvuxKLRhvtIAFLu+XjQ\nf2xAyj0fD/qPDUi55+NB/4cOkHLPx4P+YwNS7vl40P9RMApGwSgYBaNgeIFXnz5TEY023IlFow33\nkQCk3PPxoP/YgJR7Ph70HxuQcs/Hg/4PHSDlno8H/ccGpNzz8aD/o2AUjIJRMApGwfACrz59piIa\nbbgTi0Yb7iMBSLnn40H/sQEp93w86D82IOWejwf9HzpAyj0fD/qPDUi55+NB/0fBKBgFo2AUjILh\nBV59+kxFNNpwJxaNNtxHDpByz0dD/wkBKfd8NPSfEJByz0dD/4cmkHLPR0P/CQEp93w09H8UjIJR\nMApGwSgYjuDVp89URKMNd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59+kxFNNpwJxaNNtxHwSgYBaNg\nFIyCUTAKRgFJ4NWnz1REow13YtFow30UjIJRMApGwSgYBaNgFJAEXn36TEU02nAnFo023EfBKBgF\no2AUjIJRMApGAUng1afPVESjDXdi0WjDfRSMglEwCkbBKBgFo2AUkAReffpMRTTacCcWjTbcR8Eo\nGAWjYBSMglEwCkYBSeDVp89URKMNd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59+kxFNNpwJxaNNtxH\nwSgYBaNgFIyCUTAKRgFJ4NWnz1REow13YtFow30UjIJRMApGwSgYBaNgFJAEXn36TEU02nAnFo02\n3EfBKBgFo2AUjIJRMApGAUng1afPVESjDXdi0WjDfRSMglEwCkbBKBgFo2AUkAReffpMRTTacCcW\njTbcR8EoGAWjYBSMglEwCkYBSeDVp89URKMNd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59+kxFNNpw\nJxaNNtxHwSgYBaNgFIyCUTAKRgFJ4NWnz1REow13YtFow30UjIJRMApGwSgYBaNgFJAEXn36TEU0\n2nAnFo023EfBKBgFo2AUjIJRMApGAUng1afPVESjDXdi0WjDfRSMglEwCkbBKBgFo2AUkAReffpM\nRTTacCcWjTbcR8EoGAWjYBSMglEwCkYBSeDVp89URKMNd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59\n+kxFNNpwJxaNNtxHwSgYBaNgFIyCUTAKRgFJ4NWnz1REow13YtFow30UjIJRMApGwSgYBaNgFJAE\nXn36TEU02nAnFo023EfBKKAukHLP/z8KRsEoGAWjYBQMa/Dq02cqotGGO7FotOE+CkYBtYCUez4a\n+j8KRsEoGAWjYBQMR/Dq02cqotGGO7FotOE+CkYB5UDKPR8P+j8KRsEoGAWjYBQML/Dq02cqotGG\nO7FotOE+CkYB5UDKPR8P+j8KRsEoGAWjYBQML/Dq02cqoiHacL/YbMaAGyQu//T5FbURw2jDfRSM\nAoqBlHs+HvR/FIyCUTAKRsEoGF7g1afPVERDrOG+PJGBBJC47tWnz9RCDKMN91EwCigDUu75BNH/\nUTAKRsEoGAWjYBiBV58+UxENoYY78ii7WfP5z68+4ULr4hhgwKzz5KfPr6iBGEYb7qNgFFAGpNzz\nCaL/o2AUjIJRMApGwTACrz59piIaOg33NdDB9rg1n199IgKd7zRhYGBgYDDpuPjq02fKEcNow30U\njALKgJR7PkH0fxSMglEwCkbBKBhG4NWnz1REQ6bhDlkkQ1Ir/GSHGQMD1QbdGUYb7qNgFFAGpNzz\nCaL/o2AUjIJRMApGwTACrz59piIaKg13yOoX/CtkMBB00J06e1UZRhvuo2AUUAyk3PPxoP+jYBSM\nglEwCkbB8AKvPn2mIhpaDXdSm+Dk6cKOGEYb7qNgFFAMpNzz8aD/o2AUjIJRMApGwfACrz59piIa\nWg330RH3UTAKhjaQcs/Hg/6PglEwCkbBKBgFwwu8+vSZimioNNyhR8qMrnEfBaNgGAAp93w09H8U\njIJRMApGwSgYjuDVp89UREOl4Q5rhRM/6A4dbietrY8HMYw23EfBKBgFo2AUjIJRMApGASng1afP\nVERDpuEOH3RnYGAgtPoFsq6GgYGBgVrD7a8+jTbcR8EoGAWjYBSMglEwCkYBaeDVp89UREOo4f75\nFexQSGLB6M2po2AUjIJRMApGwSgYBaNg4MCrT5+piIZYwx2GkMbUsQD84/FkIobRhvsoGAWjYBSM\nglEwCkbBKCAFvPr0mYpoiDbcBwAxMIyG1SgaRaNoFI2iUTSKRtEoGjA02hhFIAZC4NWnz6NoFI2i\nUTSKRtEoGkWjaBQNCBptjBKLGEaXyoyCUTAKRsEoGAWjYBSMAlLAq0+fqYiGT8Mddd8q0adGEo0Y\nRhvuo2AUjIJRMApGwSgYBaOAFPDq02cqoiHXcEfelgrfhIosiASodxbkq0+jDfdRMApGwSgYBaNg\nFIyCUUAaePXpMxXRkGq4r0EZVGdgYGBgMGs+Dx9rh7fjPyOa8tQ7EZJhtOE+CkbBKBgFo2AUjIJR\nMApIAa8+faYiGjoNd9hNqHFrPr/69PkV/C5VMzMTBgYGlFY7BEEvbIKrpxAxjDbcR8EoGAWjYBSM\nglEwCkYBKeDVp89UREOm4Q4ZVjfpuPjq02c4gggyYIhDEWSEnkqD7gyjDfdRMApGwSgYBaNgFIyC\nUUAKePXpMxXRUGm4Q4bPzZrRtpxCmuaY4hAEHaTHHIwnBzGMNtxHwSgYBaNgFIyCUTAKRgEp4NWn\nz1REQ6XhDlmzjtkEh4jjaLhDZTF1kYMYRhvuo2AUjIJRMApGwSgYBaOAFPDq02cqomHdcB8dcR8F\no2AUjIJRMApGwSgYBQMHXn36TEU0VBrukKUyJO40hSykodKhkAyjDfdRMApGwSgYBaNgFIyCUUAK\nePXpMxXRUGm4w8+QIb4VDm3rY9+3SjpiGG24j4JRMApGwSgYBaNgFIwCUsCrT5+piIZMwx22KoaB\nqKUvkLF2BpIa+gQQw2jDfRSMglEwCkbBKBgFo2AUkAJeffpMRTSEGu6fkdrueMbRoQPtDAwMRDXx\niUYMow33UTAKRsEoGAWjYBSMglFACnj16TMV0dBquH9+BVszg2exO+RwdzwKyEMMow33UTAKRsEo\nGAWjYBSMglFACnj16TMV0dBruA8UGm24j4KhC6Tc8/+PglEwCkbBKBgFo4Du4NWnz1REow13YtFo\nw30UDEUg5Z6Phv6PglEwCkbBKBgFo4Be4NWnz1REow13YtFow30UDC0g5Z6PB/0fBaNgFIyCUTAK\nRgHtwatPn6mIRhvuxKLRhvsoGFpAyj0fD/o/CkbBKBgFo2AUjALag1efPlMRjTbciUWjDfdRMLSA\nlHs+HvR/FIyCUTAKRsEoGAW0B68+faYiGm24E4tGG+6jYAgBKfd8guj/KBgFo2AUjIJRMApoDF59\n+kxFNNpwJxaNNtxHwRACUu75BNH/UTAKRsEoGAWjYBTQGLz69JmKaLThTiwabbiPgiEEpNzzCaL/\no2AUjIJRMApGwSigMXj16TMV0WjDnVg02nAfBUMISLnnE0T/R8EoGAWjYBSMglFAY/Dq02cqotGG\nO7FotOE+CoYWkHLPx4P+j4JRMApGwSgYBaOA9uDVp89URKMNd2LRaMN9FAwtIOWejwf9HwWjYBSM\nglEwCkYB7cGrT5+piEYb7sSi0Yb7KBhaQMo9Hw/6PwpGwSgYBaNgFIwC2oNXnz5TEY023IlFow33\nUTAUgZR7Phr6PwpGwSgYBaNgFIwCeoFXnz5TEY023IlFow33UTAKRsEoGAWjYBSMglFAEnj16TMV\n0WjDnVg02nAfBaNgFIyCUTAKRsEoGAUkgVefPlMRjTbciUWjDfdRMApGwSgYBaNgFIyCUUASePXp\nMxXRaMOdWDTacB8Fo2AUjIJRMApGwSgYBSSBV58+UxGNNtyJRaMN91EwCkbBKBgFo2AUjIJRQBJ4\n9ekzFdFow51YNNpwHwWjYBSMglEwCkbBKBgFJIFXnz5TEY023IlFow33UTAKRsEoGAWjYBSMglFA\nEnj16TMV0WjDnVg02nAfBaNgFIyCUTAKRsEoGAUkgVefPlMRjTbciUWjDfdRMApGwSgYBaNgFIyC\nUUASePXpMxXRaMOdWDTacB8Fo2AUjIJRMApGwSgYBSSBV58+UxGNNtyJRaMN91EwCkbBKBgFo2AU\njIJRQBJ49ekzFdFow51YNNpwHwWjYBSMglEwCkbBKBgFJIFXnz5TEY023IlFDKNgFIyCUTAKRsEo\nGAWjYBSQAl59+kxFRGXjRhEcUT2qRtGQQKPxPmLRaNSPTDQa7yMWjUb9iEUDG/UDaffwRgMbr6No\noNBovI9YNBr1IxONxvuIRaNRP2LRwEb9QNo9vNHAxusoGig0Gu8jFo1G/chEo/E+YtFo1I9YNLBR\nP5B2D280sPE6igYKjcb7iEWjUT8y0Wi8j1g0GvUjFg1s1A+k3cMbDWy8jqKBQqPxPmLRaNSPTDQa\n7yMWjUb9iEUDG/UDaffwRgMbr6NooNBovI9YNBr1IxONxvuIRaNRP2LRwEb9QNo9vNHAxusoGig0\nGu8jFo1G/chEo/E+YtFo1I9YNLBRP5B2D280sPE6igYKjcb7iEWjUT8y0Wi8j1g0GvUjFg1s1A+k\n3cMbDWy8jqKBQqPxPmLRaNSPTDQa7yMWjUb9iEUDG/UDaffwRgMbr6NooNBovI9YNBr1IxONxvuI\nRaNRP2LRwEb9QNo9ikbRKBpFo2gUjaJRNIpG0SgiEo023EfRKBpFo2gUjaJRNIpG0SgaAmi04T6K\nRtEoGkWja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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FNNpwJxaNNtxHwSgYBaNgFIyCUYAfSLnnE0T/R8FIAq8+faYiGm24E4tGG+6j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9nyB6\n9enzEEJS7vkE0atPnyFIyj2fIHr16fOAIyn3fILo1afPwxJJuecTRK9IVDmKBhBJuecTRK8+fR5F\no2gUDQY0vBqjayBL4Ik+eYYUxDC6VGYU0AVIuecTRP+HFJByzyeI/sOAlHs+QfR/EAAp93yC6P8w\nBVLu+QTR/////5OichQMIJByzyeI/o+CUTAKyAWvPn2mIhptuBOLGEYb7qOAXkDKPR8P+j8EgZR7\nPh70HxVIuefjQf8HDZByz8eD/g9rIOWejwf9RwJS7vl40P9RMDiAlHs+HvR/FIyCUUABePXpMxXR\nUGm449+Nih9QZ68qw2jDfRTQC0i55+NB/4cgkHLPx4P+owIp93w86P+gAVLu+XjQ/2ENpNzz8aD/\nSEDKPR8P+j8KBgeQcs/Hg/6PglEwCigArz59piIabbgTixhGG+6jgF5Ayj0fD/o/BIGUez4e9B8V\nSLnn40H/Bw2Qcs/Hg/4PayDlno8H/UcCUu75eND/UTA4gJR7Ph70fxSMglFAAXj16TMV0VBpuMOX\nweC9CRWqZnSpzCgYDkDKPR8N/R/iQMo9Hw39xw2k3PPR0H8qASoa9f//fyn3fDT0n+6AFpYSY6aU\nez4a+o8DSLnno6H/wwIMG49AgJR7Phr6PwpGwSigGLz69JmKaOg03D99fnW+04SBgYGBAefNSqMN\n91EwjICUez4a+j8KKANS7vlo6P8QB1Lu+WjoP8VAyj0fDf0fBRhAyj0fDf0fBaNgFIwCbODVp89U\nREOq4f7p86tPF5vNGBgYGBiwLoAZbbiPgmEBpNzz8aD/o4B0IOWejwf9H4JAyj0fD/pPFpByz8eD\n/o+C////j4bSKBgFo4BE8OrTZyqiIddw//zq0+eTHdDGe9yaz68+IaHRhvsoGBZAyj0fD/o/CkgH\nUu75eND/IQik3PPxoP9kASn3fDzo/yj4////aCiNglEwCkgErz59piIakg33V5/gbXQGlGUzUMHR\nhvsoGNpAyj0fD/o/CkgHUu75eND/IQik3PPxoP9kASn3fDzo/yj4////aCiNglEwCkgErz59piIa\nsg33T58RR82YdZ789PnVp9GG+ygYDkDKPZ8g+j8KSAFS7vkE0f8hBaTc8wmi/yQCKfd8guj/iAdS\n7vkE0f9RMApGwShAAq8+faYiGtIN989IS97Nms+PNtxHwXAAUu75BNH/UUAKkHLPJ4j+Dykg5Z5P\nEP0nEUi55xNE/0c8kHLPJ4j+j4JRMApGARJ49ekzFdFQb7h/foW05N3EDMIYbbiPgiEMpNzzCaL/\no4AUIOWeTxD9H1JAyj2fIPpPIpByzyeI/o94IOWeTxD9HwWjYBSMAiTw6tNnKqLh0HB/9QnppEgG\nhtGG+ygY0kDKPZ8g+j8KSAFS7vkE0f8hBaTc8wmi/yQCKfd8guj/iAdS7vkE0f9RMApGwShAAq8+\nfaYiGi4N90+fEUveRxvuo2CIAyn3fDzo/yggHUi55+NB/4cgkHLPx4P+kwWk3PPxoP+j4P///6Oh\nNApGwSggEbz69JmKaDg13GmLRhvuo4BuQMo9Hw/6PwpIB1Lu+XjQ/yEIpNzz8aD/ZAEp93w86P8o\n+P///2gojYJRMApIBK8+faYiGm24E4tGG+6jgG5Ayj0fD/pPMaCKIUMLSLnn40H/hyCQcs/Hg/6T\nBaTc8/Gg/6Pg////o6E0CkbBKCARvPr0mYpotOFOLBptuI8COgMp93w09J9iIOWej4b+jyQg5Z6P\nhv4PcSDlno+G/lMMpNzz0dD/UYABpNzz0dD/UTAKRsEowAZeffpMRTTacCcWjTbcR8GQBlLu+XjQ\n/1Ew1ICUez4e9H8UjIJRMApGweAArz59piIabbgTi0Yb7qNgSAMp93w86P8oGGpAyj0fD/o/CkbB\nKBgFo2BwgFefPlMRjTbciUWjDfdRMKSBlHs+HvR/FAw1IOWejwf9HwWjYBSMglEwOMCrT5+piEYb\n7sSi0Yb7KBi6QMo9nyD6PwqGDpByzyeI/o+CUTAKRsEoGATg1afPVESjDXdi0WjDfRQMXSDlnk8Q\n/R8FQwdIuecTRP9HwSgYBaNgFAwC8OrTZyqi0YY7sWi04T4Khi6Qcs8niP6PgqEDpNzzCaL/o2AU\njIJRMAoGAXj16TMV0WjDnVg02nAfBUMXSLnnE0T/R8HQAVLu+QTR/1EwCkbBKBgFgwC8+vSZimi0\n4U4sGm24j4IhDaTc8/Gg/6NgqAEp93w86P8oGAWjYBSMgsEBXn36TEU02nAnFo023EfBkAZS7vl4\n0P9RMNSAlHs+HvR/FIyCUTAKRsHgAK8+faYiGm24E4tGG+6jYEgDKfd8POj/KBhqQMo9Hw/6PwpG\nwSgYBaNgcIBXnz5TEY023IlFow33UTAMgJR7Phr6P/LAcPK1lHs+Gvo/CkbBKBgFo2AwgVefPlMR\njTbciUWjDfdRMAqGOpByz0dD/0fBKBgFo2AUjAJaglefPlMRjTbciUWjDfdRMAqGLpByz8eD/o+C\nUTAKRsEoGAW0Aa8+faYiGm24E4tGG+6jYBQMXSDlno8H/R8Fo2AUjIJRMApoA159+kxFNNpwJxaN\nNtxHwSgYukDKPR8P+j8KRsEoGAWjYBTQBrz69JmKaLThTiwabbiPglEwRIGUez5B9H8UjIJRMApG\nwSigAXj16TMV0WjDnVg02nAfBaNgiAIp93yC6P8oGAWjYBSMglFAA/Dq02cqotGGO7FotOE+CkbB\nEAVS7vkE0f9RMApGwSgYBaOABuDVp89URKMNd2LRaMN9FIyCIQqk3PMJov+jYBSMglEwCkYBDcCr\nT5+piEYb7sSi0Yb7KBgFQxdIuefjQf9HwSgYBaNgFIwC2oBXnz5TEY023IlFow33UTAKhi6Qcs/H\ng/6PglEwCkbBKBgFtAGvPn2mIhptuBOLRhvuo2AUDF0g5Z6PB/0fBaNgFIyCUTAKaANeffpMRTTa\ncCcWjTbcR8EoGOpAyj0fDf0fBaNgFIyCUTAKaAleffpMRTTacCcWjTbcR8EoGAWjYBSMglEwCkYB\nSeDVp89URKMNd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59+kxFNNpwJxaNNtxHwSgYBaNgFIyCUTAK\nRgFJ4NWnz1REow13YtFow30UjIJRMApGwSgYBaNgFJAEXn36TEU02nAnFo023EfBKBgFo2AUjIJR\nMApGAUng1afPVESjDXdi0WjDfRSMglEwCkbBKBgFo2AUkAReffpMRTTacCcWjTbcR8EoGAWjYBSM\nglEwCkYBSeDVp89URKMNd2LRaMN9FIyCUTAKRsEoGAWjYBSQBF59+kxFNNpwJxaNNtxHwSgYBaNg\nFIyCUTAKRgFJ4NWnz1REow13YtFow30UjIJRMApGwSgYBaNgFJAEXn36TEU02nAnFo023EfBKBgF\no2AUjIIhB6Tc8/+PglEwcODVp89URKMNd2LRaMN9FIyCUTAKRsEoGEJAyj0fDf0fBaOA7uDVp89U\nRKMNd2LRaMN9FIyCUTAKRsEoGBJAyj0fD/o/CkYBHcGrT5+piEYb7sSi0Yb7KBgFo2AUjIJRMCSA\nlHs+HvR/FIwCOoJXnz5TEY023IlFow33UTAKRsEoGAWjYEgAKfd8POj/KBgFdASvPn2mIhptuBOL\nRhvuo2AUjIJRMApGweAHUu75BNH/UTAK6AVeffpMRTTacCcWjTbcR8EoGAWjYBSMgsEPpNzzCaL/\no2AU0Au8+vSZimi04U4sGm24j4JRMApGwSgYBYMfSLnnE0T/R8EooBd49ekzFdFow51YNNpwHwWj\nYBSMglEwCgY/kHLPJ4j+j4JRQC/w6tNnKqLRhjuxaLThPgpGwSgYBaNgFAwJIOWejwf9HwWjgI7g\n1afPVESjDXdi0WjDfRSMglEwCkbBKBgSQMo9Hw/6PwpGAR3Bq0+fqYhGG+7EotGG+ygYBaNgFIyC\nUTAkgJR7Ph70fxSMAjqCV58+UxGNNtyJRaMN91EwCkbBKBgFo2AIASn3fDT0fxSMArqDV58+UxGN\nNtyJRaMN91EwCkbBKBgFo2AUjIJRQBJ49ekzFdFow51YNNpwHwWjYBSMglEwCkbBKBgFJIFXnz5T\nEY023IlFow33UTAKRsEoGAWjYBSMglFAEnj16TMV0WjDnVg02nAfBaNgFIyCUTAKRsEoGAUkgVef\nPlMRjTbciUWjDfdRMApGwSgYBaNgFIyCUUASePXpMxXRaMOdWDTacB8Fo2AUjIJRMApGwSgYBSSB\nV58+UxGNNtyJRaMN91EwCkbBKBgFo2AUjIJRQBJ49ekzFdFow51YNNpwHwWjYBSMglEwCkbBKBgF\nJIFXnz5TEY023IlFow33UTAKRsEoGAWjYBSMglFAEnj16TMV0WjDnVg02nAfBaNgFIyCUTAKRsEo\nGAUkgVefPlMRjTbciUWjDfdRMAqGBxi99nwUjIJRMApGAd3Aq0+fqYhGG+7EotGG+ygYBUMdSLnn\no6H/o2AUjIJRMApGAS3Bq0+fqYhGG+7EotGG+ygYBUMXSLnn40H/R8EoGAWjYBSMAtqAV58+UxGN\nNtyJRaMN91EwCoYukHLPx4P+j4JRMApGwSgYBbQBrz59piIabbgTi0Yb7qNgFAxdIOWejwf9HwWj\nYBSMglEwCmgDXn36TEU02nAnFo023EfBKBiiQMo9nyD6PwpGwSgYBaNgFNAAvPr0mYpotOFOLBpt\nuI+CUTBEgZR7PkH0fxSMglEwCkbBKKABePXpMxXRaMOdWDTacB8Fo2CIAin3fILo/ygYBaNgFIyC\nUUAD8OrTZyqi0YY7sWi04T4KRsEQBVLu+QTR/1EwCkbBKBgFo4AG4NWnz1REow13YtFow30UjIKh\nC6Tc8/Gg/6NgFIyCUTAKRgFtwKtPn6mIRhvuxKLRhvsoGAVDF0i55+NB/0fBKBgFo2AUjALagFef\nPlMRjTbciUWjDfdRMAqGLpByz8eD/o+CUTAKRsEoGAW0Aa8+faYiGm24E4tGG+6jYBQMdSDlno+G\n/o+CUTAKRsEoGAW0BK8+faYiGm24E4tGG+6jYBSMglEwCkbBKBgFo4Ak8OrTZyqi0YY7sWi04T4K\nRsEoGAWjYBSMglEwCkgCrz59piIabbgTi0Yb7qNgFIyCUTAKRsEoGAWjgCTw6tNnKqLRhjuxaLTh\nPgpGwSgYBaNgFIyCUTAKSAKvPn2mIhptuBOLRhvuo2AUjIJRMApGwSgYBaOAJPDq02cqotGGO7Fo\ntOE+CkbBKBgFo2AUjIJRMApIAq8+faYiGm24E4tGG+6jYBSMglEwCkbBKBgFo4Ak8OrTZyqi0YY7\nsWi04T4KRsEoGAWjYBSMglEwCkgCrz59piIabbgTi0Yb7qNgFIyCUTAKRsEoGAWjgCTw6tNnKqLR\nhjuxaLThPgpGwSgYBaNgFIyCUTAKSAKvPn2mIhptuBOLRhvuo2AUjIJRMApGwSgYBaOAJPDq02cq\notGGO7FotOE+CkbBKBgFo2AUjIJRMApIAq8+faYiGm24E4tGG+6jYBSMglEwCkbBKBgFo4Ak8OrT\nZyqi0YY7sWi04T4KRsEoGAWjYBSMglEwCkgCrz59piIabbgTi0Yb7qNgFIyCUTAKRsEoGAWjgCTw\n6tNnKqIh2nC/2GzGgBskLv/0+RW1EcNow30UjIJRMApGwSgYBaNgFJACXn36TEU0xBruyxMZSACJ\n6159+kwtxDDacB8Fo2AUjIJRMApGwSgYBaSAV58+UxENoYY78ii7WfP5z68+4ULr4hhgwKzz5KfP\nr6iBGEYb7qNgFIyCUTAKRsEoGAWjgBTw6tNnKqKh03BfAx1sj1vz+dUnItD5ThMGBgYGBpOOi68+\nfaYcMYw23EfBKBgFo2AUjIJRMApGASng1afPVERDpuEOWSRDUiv8ZIcZAwPVBt0ZRhvuo2AUjIJR\nMApGwSgYBaOAFPDq02cqoqHScIesfsG/QgYDQQfdqbNXlWG04T4KRsEoGAWjYBSMglEwCkgBrz59\npiIaWg13Upvg5OnCjhhGG+6jYBSMglEwCkbBKBgFo4AU8OrTZyqiodVwHx1xHwWjYBSMglEwCkbB\nKBgFQwa8+vSZimioNNyhR8qMrnEfBaNgFIyCUTAKRsEoGAVDBbz69JmKaKg03GGtcOIH3aHD7aS1\n9fEghtGG+ygYBaNgFIyCUTAKRsEoIAW8+vSZimjINNzhg+4MDAyEVr9A1tUwMDAwUGu4/dWn0Yb7\nKBgFo2AUjIJRMApGwSggDbz69JmKaAg13D+/gh0KSSwYvTl1FIyCUTAKRsEoGAWjYBQMHHj16TMV\n0RBruMMQ0pg6FoB/PJ5MxDDacB8Fo2AUjIJRMApGwSgYBaSAV58+UxEN0Yb7ACAGhtGwGkWjaBSN\nolE0ikbRKBpFA4ZGG6MIxEAIvPr0eRSNolE0ikbRKBpFo2gUjaIBQaONUWIRw+hSmVEwCkbBKBgF\no2AUjIJRQAp49ekzFdHwabij7lsl+tRIohHDaMN9FIyCUTAKRsEoGAWjYBSQAl59+kxFNOQa7sjb\nUuGbUJEFkQD1zoJ89Wm04T4KRsEoGAWjYBSMglEwCkgDrz59piIaUg33NSiD6gwMDAwMZs3n4WPt\n8Hb8Z0RTnnonQjKMNtxHwSgYBaNgFIyCUTAKRgEp4NWnz1REQ6fhDrsJNW7N51efPr+C36VqZmbC\nwMCA0mqHIOiFTXD1FCKG0Yb7KBgFo2AUjIJRMApGwSggBbz69JmKaMg03CHD6iYdF199+gxHEEEG\nDHEogozQU2nQnWG04T4KRsEoGAWjYBSMglEwCkgBrz59piIaKg13yPC5WTPallNI0xxTHIKgg/SY\ng/HkIIbRhvsoGAWjYBSMglEwCkbBKCAFvPr0mYpoqDTcIWvWMZvgEHEcDXeoLKYuchDDaMN9FIyC\nUTAKRsEoGAWjYBSQAl59+kxFNKwb7qMj7qNgFIyCUTAKRsEoGAWjYODAq0+fqYiGSsMdslSGxJ2m\nkIU0VDoUkmG04T4KRsEoGAWjYBSMglEwCkgBrz59piIaKg13+BkyxLfCoW197PtWSUcMow33UTAK\nRsEoGAWjYBSMglFACnj16TMV0ZBpuMNWxTAQtfQFMtbOQFJDnwBiGG24j4JRMApGwSgYBaNgFIwC\nUsCrT5+piIZQw/0zUtsdzzg6dKCdgYGBqCY+0YhhtOE+CkbBKBgFo2AUjIJRMApIAa8+faYiGloN\n98+vYGtm8Cx2hxzujkcBeYhhtOE+CkbBKBgFo2AUjIJRMApIAa8+faYiGnoN94FCow33UTAKRsEo\nGAWjYBSMglFAEnj16TMV0WjDnVg02nAfBaNgFIyCUTAKRsEoGAUkgVefPlMRjTbciUWjDfdRMApG\nwSgYBaNgFIyCUUASePXpMxXRaMOdWMQwCkbBKBgFo2AUjIJRMApGASng1afPVERUNm4UwRHVo2oU\njaKRiUaz0igaRVRBo1lpFI0iqqCBzUoDaffwRgMbr6NoFA0bNJqVRtEoogoazUqjaBRRBQ1sVhpI\nu4c3Gth4HUWjaNig0aw0ikYRVdBoVhpFo4gqaGCz0kDaPbzRwMbrKBpFwwaNZqVRNIqogkaz0iga\nRVRBA5uVBtLu4Y0GNl5H0SgaNmg0K42iUUQVNJqVRtEoogoa2Kw0kHYPbzSw8TqKRtGwQaNZaRSN\nIqqg0aw0ikYRVdDAZqWBtHt4o4GN11E0ioYNGs1Ko2gUUQWNZqVRNIqoggY2Kw2k3cMbDWy8jqJR\nNGzQaFYaRaOIKmg0K42iUUQVNLBZaSDtHt5oYON1FI2iYYNGs9IoGkVUQaNZaRSNIqqggc1KA2n3\n8EYDG6+jaBQNGzSalUbRKKIKGs1Ko2gUUQUNbFYaSLtH0SgaRaNoFI2iUTSKRtEoGkVEotGG+yga\nRaNoFI2iUTSKRtEoGkVDAI023EfRKBpFo2gUjaJRNIpG0SgaAmi04T6KRtEoGkWjaBSNolE0ikbR\nEECjDfdRNIpG0SgaRaNoFI2iUTSKhgAabbiPolE0ikbRKBpFo2gUjaJRNATQaMN9FI2iUTSKRtEo\nGkWjaBSNoiGARhvuo2gUjaJRNIpG0SgaRaNoFA0BNNpwH0WjaBSNolE0ikbRKBpFo2gIoNGG+yga\nRaNoFI2iUTSKRtEoGkVDAI023EfRKBpFo2gUjaJRNIpG0SgaAmi04U4eWhfHgAYSl3/6/Aonuths\nxoAM4tZ8fvVpFI2iUYSKzneaMDAwMJg1n//86hNWNJqVRtEowo5OdqDmDXz56PNoVhpFowgHQm/g\nEcoa9M5Kow13ktHyRAZcwKTj4qtPn9HRGhwazDpPfvr8ahSNolEERfDiEkeDYzQrjaJRhBVBe7zY\nQOK6V58+o6PRrDSKRhE2hNH7hQMcg7MDkZVGG+6kIUSkosQKor+F3nZHlKeIWEcYgrVIHUWjaEQi\npC4xtob7aFYaRaMIO4L1eEdrpVE0iihB8FY4UlZCZA0kQSgaoKw02nAnCcHKRyzxAS8lEfH3CtEW\nQRF8hYhabA2UUTSKRh6C5QgIwJIvRrPSKBpFWBGurPEKhxRWwVejWWkUjXQEa8VhNNBhWQN9DcxA\nZaXRhjspCNobwxEZUFmkqIX1xtAHPD59hvcBsEmNolE0whA0p5g1d0AyEUYWgyrAml9Gs9IoGsEI\nljUQ9Q4yGq2VRtEoIhbhTv9Ycw1WQSjCbRQ10GjDnQSEq3cFRZixCC00MVohnz6/gpuG0bcbRaNo\nhCFoGRe3BneWwSX+6fOr0aw0ikYwgo3t4aiVMNFoVhpFowg7gtZEiCYcHGG27j4NZFYabbhTD0Fj\nkSFuzedXnz6/IlSk4pcdRaNohCBoAQdZfgbNROhFIf7Mgl92FI2i4Ytgk/uQ7EMEwp9Z8MuOolE0\nrBEsN2G0tmH5AtG6e4UQxJ5Z8MtSiEYb7lRD0PYHUpsDKoKRCKAIRxtlFI2iEYQguQCeRyBcjEwx\nmpVG0SjChtDGCKFcGMDSaBjNSqNoFOFEsJF15AwCa4IzoHWPBzArjTbcqYSgkYQctTh7b1AE1UKT\neB1Fo2gIIGgpiZQFsGeK0aw0ikYRVgRtqcetgTUjMACsTQ9Bo1lpFI0i/Aiap9AAaj76PLBZabTh\nTg0EjSEGBpQRjoGM11E0igY9gmYQlAIRe6aAqhzNSqNoFKEgaNeXwcQMkkOQKyDESCFSFhvNSqNo\nFOFD8FyDDtCzzEBmpdGGO8UIGj0MDAwMcUjrnwY2XkfRKBrkCDpAiJih+vzqE65MMZqVRtEowoZg\nDXcGBoys9OnzK0QrBJ41RrPSKBpFOBG0VmJAzU2IXIacLwYyK4023ClCsGKRgQG91f55YON1FI2i\nQY2giR9lgPDVJ7g4WqYYzUqjaBRhQ4gmBUZWgiLovD9s0H00K42iUYQDQRM/PLMgI2g+Qso4A5mV\nRhvu5CNE5wxH3EAVDES8jqJRNHgRtLWBLeXjyBSjWWkUjSJsCLM9gYZgzQvYCOJoVhpFowgrgmYN\nXH1gjKwBVT8QWWm04U4egpWGDAw4oxkxHo9dAX7ZUTSKhiuCpXwiAKy1AdOCPbPglx1Fo2j4IlhN\nhKv1AFcwmpVG0SjCh9BzCjqCjjch1lbgzyz4ZSlEow13MhBskIMBT3H5+dUnAl0uAt21UTSKhimC\nlWhEAHgZOpqVRtEowoYIJX5ocwQx+z+alUbRKMKCoDkFUemgIYyG+wBmpdGGO6kIqdWOK4LhCBbT\niEITgaDmYJMaRaNopCJcReFoVhpFowgbgneDEe0JZATLOAhZmAi2/DKalUbRyEXQpjauMXLMumng\nstJow50kBOuTMTAQbrV/+vwKd1KAlbZIiWAUjaJRhFk4wtBoVhpFowgbgjYRsI7tYc01WAVfjWal\nUTTCEbT2wdraxp7LBiorjTbcSUCwyGAgstX+6hOiT8aAFLVwc7Clj1E0ikYwghad2Mq70aw0ikYR\nVgTNNQw4WhVIw+0QNJqVRtEowobgWQaljYfILxgVE0KKrllptOFOPEIabscL0GMLXqqiAeSUMYpG\n0Sj6BM8sGOUjBEFlMcBoVhpFIxzhyhqY9REE4VI/mpVG0YhG+Jp56B1gCBqIrDTacCcaIbpWBAC2\nghI9NWBPAaNoFI1wBC0EcTTcP30ezUqjaBThQojxQghAHYDHQKNZaRSNImwIs7FHoBVO76w02nAf\nRaNoFI2iUTSKRtEoGkWjaAig0Yb7KBpFo2gUjaJRNIpG0SgaRUMAjTbcR9EoGkWjaBSNolE0ikbR\nKBoCaLThPopG0SgaRaNoFI2iUTSKRtEQQKMN91E0ikbRKBpFo2gUjaJRNIqGABptuI+iUTSKRtEo\nGkWjaBSNolE0BNBow30UjaJRNIoGMYKdTUbrI8ZG0SgaRaNoFA1+NNpwH0WjaBSNokGMRhvuo2gU\njaJRNIpgaLThPopG0SgaRaNoFI2iUTSKRtEQQKMN91E0ikbRKBpFo2gUjaJRNIqGABptuI+iUTSK\nRtEoGkWjaBSNolE0BNBow30UjaJRNIqoimCr0uHApOPiq0+fsaM1iQxIAItKmGlY1rjDpGDArPn8\n51ef0NC6OAYGBpjJJzvMGJAARBA7IugwOCLKGXgRugl4raOuw6BqEpd/+rwcyWCU0IaqgQNs5oyi\nUTSKRhFd0GjDfRSNolE0iqiGkBt/qACztXexGaUVDQeJyz99fgVHsFYjSlMSj0VmnSc/fX6FQPCG\neyeEgQ7Q1X8m1mGfPr8iwRk4EU4TyA6xT59f4TE2cd2rT58RCBq8iXHI6pEcj9McJDWjaBSNolFE\nNzTacB9Fo2gUjSIqIdhgMMowMLRpiN7Ug7cIEYph2lEalzDtyA13LHo/4dAOa7hDAJIhiEYwiiFY\nDcduMjaVn3Aqxo5gilFMgHmZzBBDUoksjlUQYRfC2IvL10AYCC0wqc+vPuG0dBSNolE0iuiARhvu\no2gUjaJRRB0EbeehNjdffYI39ZCGkGHtRZQWIWIpCxaViDY3pggcYZFCNNyRBCEI1nZHdjDMBHIc\nBkd4pFARTUMMTeUrvIqxuAEmhcUXeKRG0SgaRaOIlmi04T6KRtEoGkXUQdBmKLaVG2gI1oLEUInZ\nIsQQgdqC2dD89PkVXBYxGAxruGNTD1WM5AziHQbVi83YV3BZhDOwI6gyTOswEMkOw1T56TM8NBBt\neph2hAgMQc2hzIOjaBSNolFEXTTacB9Fo2gUjSLqIFjjkoEBW0MQGUGbfTgahSgI1rKENUyhw+S4\nzIe5Ad5shTZVsTYxMRQT7zBSnYEdwZQxMOA2CoJIdRhW/77CNAc9eOEIag4uV8FcTsCDo2gUjaJR\nRF002nAfRaNoFI0iaiFoaw8FwNuICARThqNxiYLQW5awhjgBAG9QQtVjbYBitD6JdxjUWEIAbjIu\nBLMRGVAUYlCHYfXvK0wvowcvHEHNIQRg5oyiUTSKRhFd0GjDfRSNolE0iqiKoOuz0QFS05D4Zihm\ny5LUBiVUPdaGLHorlgSHQY0lBOAm40XUDDGow7D69xWml9GDF46g5hACMHNG0SgaRaOILmi04T6K\nRtEoGkW0QRjtUVhrkvhmKGbLEtqghBlFEOFTj96KJcFh+IwlH1EhxAg4jOilMgTMGUWjaBSNogFB\now33UTSKRtEoojGCt0dh7UX05iMCYbRQ0VuW0AYlQgEBBFWPtQGK0XAn3mFQY2FcaiPyQwyNi47Q\nzUEPXjiisQdH0SgaRaOILDTacB9Fo2gUjSKqIHxNRrQmMhoXCUHbi4h2NkbLEtr0xKIXK8IwEAlh\nOgNTBIbQzSHRGVgRTUIMr8OgihE2YgQvHOE1ZxSNolE0igYGjTbcR9EoGkWjiDoIT1MPKoUx0Atv\nbkIRdKQZy0HjiJYlVA0DovWJQLCmMNwiWFMV3aJPn19hbQ3DrENXD7UUyWFQEQbinIEdQYMF2QEw\nBJWCm0C8w3CphJuJTTEieOEIajIDJR4cRaNoFI0i6qLRhvsoGkWjaBRRCcFagQyoLVFY+xilKQlr\nRCIJYm0pwsxEblnC9aI2HKFtdAYUxVBBhC1ICOYwFNfCDUdoweowJJVEOAMHgvmOAdUNMIchuQHJ\nOoQgQYchiWMVhDsAq1MRWsj24CgaRaNoFFEVjTbcR9EoGkWjiHoI3pTEBMjtxU+fEaO2aACljYir\nZYlDLwMDA7pKaCsT0dhFQrD2MUqjGafhaA779BmnSgYGBnRn4EZUD7FPn18ht7nRAJqZ2IMXjnDY\nyMDAgFPLKBpFo2gU0RCNNtxH0SgaRaOIugiztYfWMkZCqM1WLM1rPC1LmBQCoLVKP30mq+H++dUn\nIhwGR0Q5Az+iaojBEbrDsJkJU4MleOEIpgYBSPbgKBpFo2gUUQeNNtxH0SgaRaNoFI2iUTSKRtEo\nGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG0Sga\nRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTacB9F\no2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSKRtEo\nGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG0Sga\nRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTacB9F\no2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSKRtEo\nGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG0Sga\nRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTacB9F\no2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSKRtEo\nGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG0Sga\nRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTacB9F\no2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSKRtEo\nGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG0Sga\nRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTacB9F\no2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwABALp5N+3Yz/qiAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%stata -s graph_session\n",
    "use http://www.ats.ucla.edu/stat/stata/notes/hsb2, clear\n",
    "graph twoway scatter read math, name(a, replace) title(\"Graph a\")\n",
    "\n",
    "graph twoway scatter math science, name(b, replace) title(\"Graph b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Python lists as Stata macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many situations it is convenient to define values or variable names in a Python list or equivalently in a Stata macro.  \n",
    "The **`-i or --input`** argument makes a Python list available for use in Stata as a local macro.  \n",
    "For example, **`-i main_var`** converts the Python list **`['mpg', 'rep78']`** into the following Stata macro: **``main_var'`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "main_var = ['mpg', 'rep78']\n",
    "control_var = ['gear_ratio', 'trunk', 'weight', 'displacement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "mpg rep78\n",
      "\n",
      "gear_ratio trunk weight displacement\n",
      "\n",
      "Linear regression                               Number of obs     =         69\n",
      "                                                F(6, 62)          =       8.60\n",
      "                                                Prob > F          =     0.0000\n",
      "                                                R-squared         =     0.4124\n",
      "                                                Root MSE          =     2338.1\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "         mpg |  -76.95578   84.95038    -0.91   0.369    -246.7692     92.8576\n",
      "       rep78 |   899.0818   299.7541     3.00   0.004      299.882    1498.282\n",
      "  gear_ratio |   1479.744   917.5363     1.61   0.112    -354.3846    3313.873\n",
      "       trunk |  -110.3163   80.16622    -1.38   0.174    -270.5663    49.93365\n",
      "      weight |   1.139509   1.187361     0.96   0.341    -1.233991     3.51301\n",
      "displacement |   17.82274   8.523647     2.09   0.041     .7842094    34.86126\n",
      "       _cons |  -5163.323   4965.389    -1.04   0.302    -15088.99    4762.348\n",
      "------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -i main_var -i control_var -os\n",
    "\n",
    "display \"`main_var'\"\n",
    "display \"`control_var'\"\n",
    "\n",
    "regress price `main_var' `control_var', vce(robust)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modify dataset in Stata and return it to Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible create new variables or modify the existing dataset in Stata and have it returned as a Pandas dataframe.  \n",
    "In the example below the output **`-o car_df`** will overwrite the **`car_df`** previously created.  \n",
    "Note, the argument **`-np or --noprint`** can be used to supress any output below the code cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%stata -o car_df -np\n",
    "generate weight_squared = weight^2\n",
    "generate log_weight = log(weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>price</th>\n",
       "      <th>mpg</th>\n",
       "      <th>rep78</th>\n",
       "      <th>headroom</th>\n",
       "      <th>trunk</th>\n",
       "      <th>weight</th>\n",
       "      <th>length</th>\n",
       "      <th>turn</th>\n",
       "      <th>displacement</th>\n",
       "      <th>gear_ratio</th>\n",
       "      <th>foreign</th>\n",
       "      <th>weight_squared</th>\n",
       "      <th>log_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMC Concord</td>\n",
       "      <td>4099</td>\n",
       "      <td>22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11</td>\n",
       "      <td>2930</td>\n",
       "      <td>186</td>\n",
       "      <td>40</td>\n",
       "      <td>121</td>\n",
       "      <td>3.58</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>8584900.0</td>\n",
       "      <td>7.982758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMC Pacer</td>\n",
       "      <td>4749</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3350</td>\n",
       "      <td>173</td>\n",
       "      <td>40</td>\n",
       "      <td>258</td>\n",
       "      <td>2.53</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>11222500.0</td>\n",
       "      <td>8.116715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMC Spirit</td>\n",
       "      <td>3799</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2640</td>\n",
       "      <td>168</td>\n",
       "      <td>35</td>\n",
       "      <td>121</td>\n",
       "      <td>3.08</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>6969600.0</td>\n",
       "      <td>7.878534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          make  price  mpg  rep78  headroom  trunk  weight  length  turn  \\\n",
       "0  AMC Concord   4099   22    3.0       2.5     11    2930     186    40   \n",
       "1    AMC Pacer   4749   17    3.0       3.0     11    3350     173    40   \n",
       "2   AMC Spirit   3799   22    NaN       3.0     12    2640     168    35   \n",
       "\n",
       "   displacement  gear_ratio   foreign  weight_squared  log_weight  \n",
       "0           121        3.58  Domestic       8584900.0    7.982758  \n",
       "1           258        2.53  Domestic      11222500.0    8.116715  \n",
       "2           121        3.08  Domestic       6969600.0    7.878534  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve macro from Stata back into Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **`-gm`** or **`--getmacro`** argument allows a macro to be extracted from a Stata session. The macro will be added to the **`macro_dict`** dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Several (2x) macros have been added to the dictionary: macro_dict\n"
     ]
    }
   ],
   "source": [
    "%%stata -s macro_example -gm macro_1 -gm macro_2\n",
    "local macro_1 one two\n",
    "local macro_2 three four"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'macro_1': ['one', 'two'], 'macro_2': ['three', 'four']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one', 'two']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_dict['macro_1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Python working directory in Stata "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(r'C:/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set the working directory of Stata to: C:\\\n"
     ]
    }
   ],
   "source": [
    "%%stata -cwd\n",
    "display \"`c(pwd)'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Mata code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(1978 Automobile Data)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s mata_session\n",
    "sysuse auto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mata output:\n",
      "\n",
      "                  1              2              3\n",
      "    +----------------------------------------------+\n",
      "  1 |  -220.1648801    43.55851009    10254.94983  |\n",
      "    +----------------------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata --mata -s mata_session\n",
    "y    = st_data(., \"price\")\n",
    "X    = st_data(., \"mpg trunk\")\n",
    "n    = rows(X)\n",
    "X    = X,J(n,1,1)\n",
    "XpX  = quadcross(X, X)\n",
    "XpXi = invsym(XpX)\n",
    "b    = XpXi*quadcross(X, y)\n",
    "b'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPyStata 0.2 introduces the possibility to use many different Stata sessions that by default run in the background.  \n",
    "These sessions are defined using the **`-s`** or **`--session`** arguments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%stata -s session_1 -np\n",
    "local session Hello I am session 1 and I am persistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%stata -s session_2 -np\n",
    "local session Hello I am session 2 and I am persistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello I am session 1 and I am persistent\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s session_1\n",
    "display \"`session'\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello I am session 2 and I am persistent\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s session_2\n",
    "display \"`session'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session example 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example a logistic regression is performed in one cell and a postestimation (predict) is performed on this regression in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no; data in memory would be lost\n",
      "r(4);\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -s auto_session\n",
    "sysuse auto\n",
    "logit foreign weight mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%stata -s auto_session\n",
    "predict probhat\n",
    "summarize probhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session manager tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to avoid using unnecessary system resources several tools and automatic cleanup routines are included."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display all active Stata sessions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following sessions have been found:\n",
      "macro_example [active]\n",
      "main [active]\n",
      "session_2 [active]\n",
      "mata_session [active]\n",
      "graph_session [active]\n",
      "session_1 [active]\n",
      "auto_session [active]\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reveal all Stata sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revealed 7 Stata sessions.\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "reveal all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hide all Stata sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 Stata sessions have been hidden.\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "hide all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Close all Stata sessions initiated by IPyStata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following sessions have been closed:\n",
      "macro_example\n",
      "main\n",
      "session_2\n",
      "mata_session\n",
      "graph_session\n",
      "session_1\n",
      "auto_session\n",
      "Terminated unattached Stata session.\n"
     ]
    }
   ],
   "source": [
    "%%stata \n",
    "close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close all Stata sessions (**Warning! This closes <u>all</u> Stata windows**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Terminated 2 running Stata processes\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "close all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An example case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the variable **`large`** in Python and use it as the dependent variable for a binary choice estimation by Stata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car_df['large'] = [1 if x > 3 and y > 200 else 0 for x, y in zip(car_df['headroom'], car_df['length'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headroom</th>\n",
       "      <th>length</th>\n",
       "      <th>large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>218</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   headroom  length  large\n",
       "0       2.5     186      0\n",
       "1       3.0     173      0\n",
       "2       3.0     168      0\n",
       "3       4.5     196      0\n",
       "4       4.0     222      1\n",
       "5       4.0     218      1\n",
       "6       3.0     170      0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_df[['headroom', 'length', 'large']].head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "main_var = ['mpg', 'rep78']\n",
    "control_var = ['gear_ratio', 'trunk', 'weight', 'displacement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iteration 0:   log pseudolikelihood =  -39.60355  \n",
      "Iteration 1:   log pseudolikelihood = -19.307161  \n",
      "Iteration 2:   log pseudolikelihood = -13.526857  \n",
      "Iteration 3:   log pseudolikelihood = -10.999644  \n",
      "Iteration 4:   log pseudolikelihood = -10.726345  \n",
      "Iteration 5:   log pseudolikelihood = -10.723111  \n",
      "Iteration 6:   log pseudolikelihood = -10.723109  \n",
      "Iteration 7:   log pseudolikelihood = -10.723109  \n",
      "\n",
      "Logistic regression                             Number of obs     =         69\n",
      "                                                Wald chi2(6)      =      12.90\n",
      "                                                Prob > chi2       =     0.0446\n",
      "Log pseudolikelihood = -10.723109               Pseudo R2         =     0.7292\n",
      "\n",
      "                                  (Std. Err. adjusted for 69 clusters in make)\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "       large |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "         mpg |  -.5846335   .2941083    -1.99   0.047    -1.161075   -.0081918\n",
      "       rep78 |  -1.298127   1.264918    -1.03   0.305    -3.777322    1.181067\n",
      "  gear_ratio |  -1.331913   3.389448    -0.39   0.694    -7.975109    5.311283\n",
      "       trunk |   1.210178   .4830082     2.51   0.012     .2634991    2.156856\n",
      "      weight |  -.0007284   .0022358    -0.33   0.745    -.0051105    .0036536\n",
      "displacement |    .001631   .0160425     0.10   0.919    -.0298119    .0330738\n",
      "       _cons |  -.2977676    16.7841    -0.02   0.986    -33.19401    32.59847\n",
      "------------------------------------------------------------------------------\n",
      "Note: 8 failures and 0 successes completely determined.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata -d car_df -i main_var -i control_var\n",
    "\n",
    "logit large `main_var' `control_var', vce(cluster make)"
   ]
  }
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